Full AI Prompting Course with Andrew Ng · 全文文字稿

It is now 2026 and prompting AI models is very different from when CAT GBT first came out in 2022. Using AI well is one of the most impactful skills

you can develop. And people that are not yet at a cutting edge of AI usage often run Intel AI generating frustrating outputs. I want to make sure

you're an expert prompter and can take advantage of today's AI tools which are much more powerful than

they were even a year ago. Let's take a look at two different experiences. The AI novice and AI power user. Many AI experts have learned to use it to

answer hard questions. In contrast, many people, including AI noviceses, may have gotten used to using AI for simple questions as if you were

prompting it like a Google search. So you ask it, does Taco Bell still have the double deck taco? And maybe

you get an answer like that, which is fine. But if you have much harder questions, you can also ask it of the AI and give it time to think. For

example, if you're looking to buy a car, you can upload to most of the commercial services like Chai GPT, Gemini, Anthropics, Claude, or others a set

of documents including cost specs, quotes, insurance plans, and ask it what are the trade-offs for these

different cars I'm thinking about and tell it to read everything and to think hard before answering. And this can cause AI to spend many seconds or

even minutes to think and then compile a detailed report for you. I find this a huge timesaver for a lot of things I have to do. Another example, AI

power users have learned to provide the right context or the right background information to the AI to

set it up for successfully answering your question. In contrast, I see some AI noviceses use a short prompt and hope the AI will fill in the blanks.

But if you think of AI as maybe being akin to a really smart, fresh college grad, highly motivated, but that doesn't really know that much about you

yet, then a short prompt sometimes doesn't give it enough information or enough background context

answer your question accurately. So, if you tell AI, please write a good self- review to send to my boss, the AI doesn't know what you've actually

done over the last year because you haven't told it yet, and it might write a very generic self-re, which isn't that helpful. In contrast, I find that

AI power users almost have empathy for the AI. I don't want to overly anthropomorphize the AI, but if

you could put yourself in the shoes of someone getting a set of instructions from you, you can ask yourself, will they actually know enough about you

to do a good job on the task you're assigning them? So, an AI power user in comparison might upload a lot of information to the AI. Maybe give it a

screenshot of a project tracker showing what you worked on, recent project dogs, maybe voice memo

notes where you talk through the projects and then tell it to write a self- review to send to my boss. And that could do a much better job capturing

what you're most proud of. One of the things power users have learned to do is how to prompt AI to get honest feedback. A big problem with AI is it

often wants to please you. In fact, many AI systems were trained to try to make the users happy. And if

you ask it a biased question, it will often give a biased answer because it's trying to tell you what it thinks you want to hear. For example, if you

say, "I have a great business idea, mobile tie dying. Critique it." because you called it a great business idea and you're saying it's your idea, the

AI will naturally want to please you and say, "What a great idea." We sometimes call this sycopency

and it's well known that if you give even a hint of what answer you're hoping for, there's a good chance the AI will just reflect back your

preferences or your preconceptions. In contrast, AI power users tend to ask neutral questions that don't give any hint to the AI for what answer

you're hoping for or not hoping for. Or if you give it a rubric or grading criteria to tell the AI how to form the

basis for its answer, that also forces it to be more objective. For example, if you are to say, "Please analyze the following business idea

objectively, mobile tie dying." And don't just make up a bunch of things for what you think. Use the rubric or the grading criteria above, such as,

"Is there a problem? Is there a market? Do I have a competitive advantage?" If you give instructions like this

to the AI, then the AI doesn't know. Are you hoping it'll tell you it's a great idea or that it will save you from spending a lot of time on a bad

business idea? and it's much more likely to then tell you something like, "Oh, this idea is a eight out of 100 and also why the score's low." In case

you run a mobile tie-dye business, I wish you really best of luck and AI could also help ask some

useful questions to help you think through how to make the business even better. Lastly, I found that AI novices and AI power users ask AI to write in

very different ways. Novices will just ask AI to write stuff like write a blog post about the Blackberry and it will generate a bunch of text that

maybe looks like this which sounds like AI slop. There's a bunch of generic text that's just not that

interesting and takes up a lot of space. In contrast, an AI power user will often not ask the AI system to just jump in writing directly, but instead

ask the AI to first outline an article and then critique the outline and maybe iterate a few times with the outline to shape the article and only then

ask the AI to start to draft the final article. So give it a set of uploaded notes as context. An

expert may say, "I'll line a blog post about the Blackberry based on my notes so it knows what you want to talk about." And the AI may start by giving

an outline. And you might then give feedback to the AI about what you like and what you don't like about the outline. And even iterate a few times,

have a few back and forth rounds before you have an outline that you're satisfied with. And maybe

only then expand the outline into bullet points. And maybe even go back and forth a few times to critique the bullet points before you're satisfied

with that and then expand it into the final text. This type of power user workflow is much more likely to generate some text that you're happy with as

opposed to AI slop. And in this type of workflow, you're treating the AI as a thinking partner to

almost help you brainstorm and explore different options for what you might want to write. Air systems do make mistakes, but maybe fewer than most

people think, especially if you prompted well. They made a lot more mistakes back in 2022 or 2023 than they do now. But a lot of widely publicized

mistakes that AI has made, some of which went viral on social media, has made people think that AI maybe

makes even more mistakes than it actually does. There's a well positized one where people asked it, how many Rs are there in the word strawberry and

it thinks there are two Rs. And here's one that I found amusing. I want to wash my car. Should I walk or drive there? And AI says walk, which would

leave you there or wash your car. But these viral examples are not representative of AI capabilities.

In contrast, P users know that AI can deliver significant value through tasks like doing deep research and writing research reports or taking your

personal data like your health or heart rate or running time data and analyzing that for you or stuff we will talk about later even building websites

for you. I've seen being an AI PA user tremendously benefit individuals as well as their businesses.

It'll save you time and improve your professional and personal lives. They'll help you to build lots of cool things. You learn how later in these

videos and be able to prompt AI at an expert level is an highly in demand job skill no matter what job row you're in. In the rest of these videos, I

hope to take you from wherever you are today to being an AI power user. Much has been said about AI being

useful. I find using AI really fun as well, and you'll see a few examples of that in these videos, too. Now, one foundational piece of knowledge that

helps you work of AI is understanding where it gets its knowledge from so that you can better predict when it'll get something right and when you

maybe should encounter this answer. Let's go on to the next video to learn about how AI gets its

knowledge. How did you learn to write as a child? Probably it involve reading a lot of things. Well, it's the same for AI. AI systems have learned

patterns from reading large amounts of text from the internet. By understanding what's in that text the AI has read, you'll be able to predict how

they'll behave. AI models can answer questions on a variety of topics. If you were to ask, I dropped my

phone in soup. What should I do? Then hopefully you'll make some useful suggestions. Or why do cats stare at walls like they see in ghosts? My

daughter loves cats. She was actually curious about this. Turns out cats can detect subtle sounds and movements that we as humans often miss. Because

of the amount of things is read on the internet, they will even possess niche knowledge that few people

know about. They were to ask what kind of things were on the vinyl record sent into space. years ago, NASA had a spacecraft called Voyager 1 that

launched in the 1970s and is now about 25 billion miles away from Earth. But AI will know about this and be able to tell you what is on that vinyl

record. I think it's cool that NASA chose to send greetings in 55 different languages to whoever may come

across that spacecraft, if anyone does. AI models are trained on many different sources of information mainly from the internet and training on all of

these very diverse sources of knowledge produces is pre-trained knowledge. The term pre-trained is a technical term that you don't have to worry

about. It turns out AI systems are trained in multiple steps and this is one of the first steps of

training that somehow wound up being called pre-training which isn't a great term but I wouldn't worry about why we call pre-training. It's just what

AI has learned from. But these knowledge sources may include a lot of texts from social media like Reddit which will have answers to questions like

what are your mustwatch films or it may have read a book on Lego micro cities or read a Wikipedia

article on fairy bread and lots of other things or read a bunch of news articles as well as read a lot of research articles on the internet. There's a

lot of texts on internet forums and social media like Reddit and Quora. There are a lot of books that AI will have read from. There are encyclopedias

like Wikipedia, news websites, research articles, and much more. And so these trillions or tens of

trillions of words will go into training the AI models brain. Now different types of data appear with different amounts of frequency on the internet

and so this pre-trained knowledge reflects the frequency or the patterns in the training data. For example, cooking is a very universal human

experience. So there are a lot of articles on the internet on cooking. There also a lot of articles online on

celebrities on movies and so AI will have seen a lot of text on these topics. In contrast, they're more specialized topics like quazar, which is an

astronomical term referring to really bright objects in the sky powered by super massive black holes. I think they're fascinating, but they just a lot

fewer articles on quazos and on cooking on the internet. Now, while most of the internet is in

English, AI systems will also have learned from some data that's written in other languages like Cantonese. Over 80 million people speak Cantonese,

but that's far less than English and Cantonese data represents maybe less than 0.1% of all internet content. Lastly, there are things that AI models

know nothing about at all, such as your company's secret proprietary data, which hopefully is not on

the open internet, but which an AI system will therefore not have learned from. So I find that thinking about how frequent data appears on the

internet gives you a good rule of thumb for thinking about how reliable an AI systems responses are. Now because of the data the AI has learned from

sometimes it can exhibit surprising understanding of things. If you were to type very quickly, can you cook

eggs in microwave? Like shown on the left, you can actually understand this type of misspelled text very well. Pretty much as well as asking, can you

cook eggs in the microwave? And by the way, I've exploded a few eggs in the microwave myself. So, if you ever want to avoid that, feel free to ask the

AI system how to do so, so you don't have to learn the hard way. And a reason that it's so good at

understanding misspelled words is because it's actually learned from a lot of sources that could include typos. So if you look online, you will see

phrases misspelled. And that's why when you're using the system, I'm not encouraging you to use bad grammar or to misspell words. But it turns out

that if you're typing quickly and you have a few typos or even a lot of typos, don't worry too much about

it. It's pretty fine to just send a prompt to AI and not spend too much time fixing every little grammatical error. Now, the bad news is a lot of AI

sources also have misconceptions and outdated information. So, one of the skills in using AI is how to prompt it to have it give you back answers that

reflect fewer misconceptions and does not overly reflect outdated information. By understanding AI's

knowledge sources called as pre-trained knowledge, you'll be able to better predict how it will respond to your prompts. But this pre-trained

knowledge is not enough for all applications, including those that need real time information. For that, you need web search. Let's go on to the next

video to learn more. At some point, the people building the AI model had to stop his training. So there's

some last state where is information cuts off. That is the AIS read the internet only up to certain date and time and it knowledge gets frozen in time

as of that date. But of course the world moves on past that date. New things happen, movies come out and so on. Let's see how AI models handle

gathering new information using web search so that it can address questions that even relate to things

after its knowledge cut off date. If you're using one of the popular AI model providers like Jagy, Gemini, and CO, there are certain questions that

will probably trigger it to do a web search. For example, if you ask it, what is the 67 meme from 2025? There's a good chance it will search on the

internet to tell you that the 67 meme, which is pronounced 67, which is kind of fun to say that this is

a viral internet slang widely seen on a few social media platforms. And the reason it triggers a web search when you ask it, what's a 67 meme from

2025? The Q 2025 causes the AI to realize that it may benefit from more updated online information because this could be a meme that appeared on the

internet after its knowledge cut off date. Here's what I mean. A specific AI models pre-trained

knowledge is frozen in time even though the internet continues to evolve over time. And so if this line represents time, then for a long time the

internet will have had pieces of text that say 6 * 7 = 42. Text that talks about the children's joke. Why was six afraid of seven? Because 7 8 9. But

if the knowledge cut off date was at a certain moment in time and the 67 meme came after that then the

67 meme would not have been seen in the pre-trained knowledge of the AI model. So if you ask it what is the 67 meme from 2025 the AI model will

realize that it doesn't know about this 67 meme from 2025 and that it should do a web search in order to get more updated information. Take the GPD

5.4 model from OpenAI. His knowledge cut off dates was August 2025. And this graph shows how many Google web

searches there were for what does 67 mean. So this 67 meme had taken off after this GP 5.4 knowledge cut off date, which is why the model doesn't

really know about this meme. Now, there's certain types of questions that an AI will answer using its pre-trained knowledge, and there's certain types

of questions that will tend to trigger web search. For example, if you tell it, please find me a highly

rated gym near Mountain View, California. Then, what is highly rated, what may be open, and what may be closed, does change over time, and there's a

good chance that this will trigger a web search. Or if you ask it, what is the market mountain cheese row? Because this is a niche piece of

information, it's probably not read a lot of information online about this cheese row. There's a good chance

that it will search the internet in order to get you an answer. And if you're curious, this is actually a pretty fun events where people chase a

rolling wheel of cheese down the hill. Let's take a look more broadly at when an AI model needs to do some web search to gather more information to

answer your question. If you're asking what to do if you drop your phone in soup or why do cats stare walls

or walls on the Voyager one record in outer space then these questions it could probably answer using this pre-chain knowledge because these are

represented in common knowledge on the internet. But if it was ask it about current events or something happening very recently then it'll need to do

web search to get that real-time information. If you ask it location specific information and doing a web

search makes sense. Or if you ask it for other types of niche information, there's also a good chance of realize it doesn't know enough about that

topic that doing a web search to gather more information would help it give you a better answer. For most of the popular AI model providers, web

search can be triggered in either of two ways. Sometimes the AI model will decide by itself to carry a web

search or you can also explicitly trigger web search sometimes by clicking one of the buttons in a AI model providers web interface or just writing

your prompt. Please do a web search for this and it will comply and use a web search to answer your question. Not all AI models have web search

enabled, but the most popular ones that you're probably using mostly do have this capability. AI will do

better on many of the tasks you want to use it for if it does web search. And web search allows it to augment this pre-trained knowledge with more

current information. But like all web search, it can return bad sources. Let's take a look at when this is an issue and when and how to get it to use

more reliable sources to get you more reliable answers. Web search is a very valuable but imperfect

tool. Just like when you search the web yourself, you might not always find what you're looking for. It has limitations like finding old or inaccurate

sources. But you can work around these limitations to get AI to give you more accurate and up-to-date answers. Let's take a look. If you ask a AI

system, how safe are gray market peptides, which is a type of supplement, it may search online and find

posts on social media or public forum sites like Reddit and Hora. Or you may find websites that are in the business of selling peptides and so would

have a inclination to tell you that they're safe and you may get back answers that may or may not be accurate. But if you encourage the AI model to

use sources from official organizations or look at studies that are backed by rigorous science, then

it's more likely to look up resources from the World Health Organization, from the US Food and Drug Administration, from the European Medicine Agency

and so on, and hopefully give you more reliable and scientifically credible answers. Web search, whether done by a human on Google or Bing or done by

AI, has a tendency to draw from popular sources. According to one report, the most cited website by

AI model was Reddit, followed by Wikipedia, YouTube, then Google itself, Yelp, and so on. And some of these sources are more trustworthy than others.

There's just a lot of text on the internet from social media, blogs, online forums, and the amount of text from highly reliable scientifically

verified sources is just much smaller. So, if you don't steer the model in terms of what types of sources

you prefer, there's a chance that it'll tend to pull text from whatever is most available rather than what's most reliable. So that's why if you ask

it how safe are gray market peptides, it might base a lot of his answer on social media blogs and forums and only a little bit on the more reliable

sources. Whereas if you tell it to use sources from official health organizations, it may pull much

more from these reliable sources. Another limitation of web search is that sometimes web pages can be outdated. that can lead the AI model to also not

provide the most current information. A friend of AI recently helped me find places to run in Henderson, Nevada. This is a location specific niche

query and so this triggers web search and it found this list of places to go for a jog. But it turns

out that unfortunately this pull from a web page from more than two decades ago. And unfortunately, the location suggested was a school that unlike

decades ago is no longer open to the public to go running in. To help build intuition about how AI searches the web to use that information, let me

briefly explain how web search actually works under the hood. It turns out to be a multi-step process.

Imagine that you're asking questions of a customer service team of two people. There's the userfacing AI model. That's what you are talking to. And

the userfacing AI model has a second assistant AI model that it can ask for help to do web search. So when you send a prompt, you are talking to the

first model, the userfacing AI model, and it will occasionally decide to call up the assistant AI

model, the second AI to say, hey, please do a web search for me to gather more information. This assistant AI model will then search on a web search

engine very similar to Google and Bing and other web search engines that we as people might use and it will scan the return results, filter out the

relevant results and download the most relevant web pages and then summarize them. The second assistant

AI model will then present the summaries back to the first model, the userf facing AI model. And the first model will then use these summaries in

order to generate the final answer for you. You are speaking only to the userfacing AI model. And one interesting quirk to keep in mind is the

userfacing AI model has not actually read in its entirety all of the web pages it may be citing for you.

Instead is only seen summaries of those web pages. And sometimes this causes it to misinterpret what one of these underlying web pages actually says.

Which is why you may have seen funny results where AI cites a web page and says the web page justifies a conclusion. But if you look at that web page

yourself, it doesn't actually justify what the userfacing AI model says it is doing. To walk you

through one example of this process, if you ask the userfacing AI model, that's like the customer service agent talking to you. It will ask what

should I know before hiking Machu Picchu. The second model may do some web searches with phrases like mu future permits, much future weather or the

social customs and so on. And it'll then scan the returned results much like you may scan the page of

Google results to decide what's relevant and filter out irrelevant results and summarize the most relevant web pages to provide back to the first

agent that then generates the final answer for you. Now I frequently use AI models like CATV, Gemini, C and I also frequently use web search engines

like Google and Bing. When should you use a AI model and when should you use a web search engine? If you

want to quickly scan multiple sources, a search engine can be useful for that. Or if you want to navigate to a specific website but have forgotten

what's the name of that website, a web search engine can be very good for helping you find it. Or if you want to look at data in its original form,

such as if you want to buy a 2013 Honda Civic air filter, you know, you want to find a website to go to

buy that air filter. So web search engine is very good at that. In contrast, if you want to get a synthesis from multiple sources, or if you're

searching for more complex information with pros and cons that you want weighed, or if you just want to contrast multiple sources to come up with a

more thoughtful conclusion, then an AI model can do a web search and put together the results of multiple

web pages for you quite efficiently, thus maybe saving you time of having to read a lot of web pages yourself. There might be some good Google or

other web search habits that you've developed and those habits will serve you well when working with web search enabled AI models as well. Things like

looking for reliable sources and also double-checking the sources. But if you want to go beyond

searching a handful of web pages, it turns out AI models are capable of a much more extensive type of research called deep research. This is a very

powerful capability that I think is really underused by many people. Let's go on to the next video to see what it is and when and how to use a deep

researcher. Sometimes you would want your AI to synthesize not just a handful of sources but many maybe

many dozens of sources and do lots of thinking to come up with the best possible deeply researched answer to a question that you have. Popular AI chat

interfaces like tragically Gemini and CO all have a deep research mode. I found this to be a very valuable and often underutilized tool. Let's take a

look. Let's say you want to use an AI model to help you plan your Halloween haunted house. I'm

going to write a prompt to ask you to help me set up a haunted house in my front yard for Halloween and give it some information about where I am,

what's the size of my front yard, what's the experience I want. So, I give it lots of context to set it up to plan it out for me appropriately. With a

prompt like this, an AI model might come up with a research plan in which it tries to think through

what are the types of sources it needs to research. Many systems would give you an opportunity to approve or potentially edit the research plan. And

if you're happy with it, I'll often launch the research plan without updating it unless I see something that just looks really wrong. It will then go

ahead and start to do online searches. So in this example is starts by gathering Palo Alto's rules on

permits Halloween ordinances and so on. And then it will read some of those web pages and synthesize what is learned so far. And it may then decide to

do some more searches online to gather more information about fire safety guidelines. And then it may after that decide to look for decoration ideas.

So loosely follow the original research plan but also have the flexibility to keep looking deep

into certain areas if it thinks it needs that information. After searching for a while maybe many minutes it will finally write a detailed research

report for you. This process, by the way, is an example of agentic AI. And what that refers to is that through this dresearch process, the AI model

has some flexibility to make decisions by itself on what to do next, such as what additional searches,

if any, to carry out. The output of this can then be a fairly detailed and thoughtful plan with different sections outlining what you might need to

think about in terms of structural and reg framework safety and so on. If you're using Google's Gemini AI model for this, one of the neat features is

it makes it easy to take the deep researches done and hope you turn it into a web page or infographic

or handful of other things. Here's a web page that was generated by Gemini using the Gemini D researcher. And I think it's pretty neat. There's

created a web page with four different sections, pie charts for budget, pretty neat visualizations for noise ordinance. And I think it's pretty neat

that this even has a little checklist that I could use to plan out my Halloween event. To give you a sense

of how a deep researcher works, this is loosely what it does. After formulating a research plan, an AI model can actually issue many web searches at

the same time and get back multiple web pages at the same time. And this is one of the nice things about using aid researcher. It doesn't have to do

the web searches one at a time. It can do many of them at the same time, which lets it be very

efficient in fetching lots of web pages. The system can also take a look at all of these sources and quickly assess which ones are relevant and which

ones are less relevant. And based on that, it may decide whether or not to go back to do additional web searches, maybe using different web search

terms. Finally, after going around this loop a few times of doing web search, evaluating sources,

deciding whether or not to go back to get more sources, it'll hopefully decide it's done. And then lastly, take all of the pages it has downloaded and

maybe summarize and synthesize all that into a report that it adds citations to and that it then presents to you. Both web search enable AI as well as

deep research use the internet or do web search. The basic web search enable AI is good at queries

like this. Find me a highly rated gym. What's the weather in Dubai this week? Whereas deep research I would tend to use for tasks that require

synthesizing multiple views such as if I want to know what's the impact of daily steps on long-term health and if I wanted to search the most recent

scientifically justified articles and think through the answer rather than just tell me whatever people tend

to say on the internet. or if I wanted to deeply think through how does weather affect tourism in Dubai and again not just take one or two popular

answers found on a social media site but to read up on weather read up on tourism read up on Dubai and to really think through the implications to

give you a more thoughtful answer that's when deep research could be particularly helpful to give you

another framework to think through when to use web search versus deep research if you have a single question do you want answered doing work that

would take me just a few seconds based on a handful of sources. That's when web search would be helpful. And web search, as we've seen, can be

triggered either automatically or by the user. Whereas deep researchers often is trying to draw a complex set

of conclusions that may require answering multiple questions or answering multiple dimensions that relate to a question. And I think of this as doing

work that would take me minutes to maybe even hours if I was doing this manually. And I may want many sources of integration synthesize. And as we've

seen, deep researcher is usually triggered explicitly by the user unless you select it in the user

interface. Most AI models will not care a deep researcher and keep you waiting for many minutes for an answer. To recap, if you're asking, "Hope I

drop my phone in soup," it doesn't need to look up any online sources. We're not worried about freshness. It'll give you an answer in just a few

seconds. And this is good for finding basic facts, definitions, summaries for things that occur calmly on

the internet. webs may download a handful of sources and it will find relatively up-to-date information and it may take many seconds to get you back

an answer. And we've seen what types of information this is useful for. And lastly, deep research may download often dozens or more of sources. It

will get up-to-date information and it will spend many minutes or longer to get back an answer. and is

great at answering complex question that involve synthesizing many sources of knowledge. Finding information is one of the most common tasks that

people use AI models for. You've seen three different paths that you can take advantage of for this type of information finding task. You could use

just a pre-train knowledge or web search or deep research. And we also walk through how and when to use

these different options. I want to make sure that you have good intuitions about when to use each of these options. So, let's go on to take a look

next at a practice hands-on lab for this module, which I think you'll find a fun way to compare and contrast what each of these three options do, and

more importantly will also help you hone your intuition on when to use each. In this module, you learn

how to use AI models to find information. In this practice lab, you can explore how web search, deep research, and different prompts affect the AI

models output. Let's take a look. When you open up the lab, it starts off with a tutorial on how to use the lab. And I'm just going to close it out

here. We can always access this tutorial again via this button up here. And what I hope you do is follow

the instructions written here. And when you're done, click mark as complete to mark this item as completed. First, these buttons down here correspond

to different things you might try. So, this one, current events, compares a question with and without web search. So, what's the 67 meme? This on the

left is without web search. The one on the right is with web search enabled. And if you compare then

without web search it gives these answers. It doesn't know about the meme. But with web search it tells you what is this 67 meme as well as the

origins of this meme. And if you want, you can also follow up and ask, "How do I use 67 appropriately in a black tie tuxedo body?" And then hit this

red button to go see his answer. Let's go back to the homepage by clicking new chat up here. And I hope you

try out the other examples such as find me a highly rated gym and hit compare or when's the next Avengers movie scheduled or what major news happened

today in the US or feel free to enter your own country and compare these results with and without web search. This example over here shows the

difference between web search denoted by this globe icon versus steep researcher denoted by this microscope

icon. So how safe by gray market peptides use high quality sources and this would give you a sense of what web search results looks like versus deep

researcher results. I hope you run it and see what results you get. One more example. If I want to ask, can I keep my rocket propelled monster truck

in my garage? If you ask it this question, the AI model may or may not know the answer. But if you

were to also upload the lease agreement, then maybe your lease agreement, which states the terms under which you're renting your place, may have

restrictions on that. And so you'll be able to see different answers depending on whether or not you upload additional information. In this case, a

lease agreement that is helpful for the AI to give you a thoughtful answer. We haven't talked much yet

about uploading your own files in this module, but this is something we'll dive into more deeply later in these videos, but feel free to play with

this. Now, lastly, one fun example. Here is a version of why do cats stare at walls with lots of typos. Here's one with nicely formatted grammar, no

typos. And you'll find that the answers are maybe surprisingly similar that the AI system is pretty good

at answering a question like this, even if it's lots of typos. Once you tried out these examples reflected by these buttons down here, come over your

own. Like, is the weather good for a picnic in Palo Alto today? And try this with or without web search and see what answers you get. Or pick your own

example and try comparing the results you get using web search. And if you want, uploading your own

files. So that's it for module one of this course. Great job getting this far. I hope you enjoy playing around with the lab. Next, please join me in

the next module where you hear about using AI as a thought partner, including having it brainstorm with you and explore ideas with you. This has

helped me shape the direction of many projects, and I'm confident you find it useful, too. and we'll also

explore getting AI to help you with your writing and editing. I'll see you in the next module.

One of the most helpful uses of AI is as a thought partner. When I'm trying to think through a complex problem or make a complex decision, it's nice

to have a human expert as if our partner that is someone to talk things through with. But if there isn't a human expert readily available, AI, which

actually knows a lot about a lot of things, can be a really good resource for this. We'll go through

together multiple examples of this, but to get started, brainstorming is one great such use case. Now, I know a lot of people ask AI to help

brainstorm lists of ideas, but they're more effective ways to use it as a brainstorming partner than just having it generate a list. Let me show you

what I mean. According to data released by OpenAI analyzing chat GPT conversations, about half of Chat GPD

chats are asking for writing and practical guidance. And in fact, creative ideation accounts for 3.9% almost 4% of all chats. I found using AI to help

me brainstorm to be really valuable. Let me share with you some ways to do so. AI can be pretty good at generating options. There's a common

creativity test which asks people to name 200 potential users for a brick. So give it a brick like this. How

many uses can you think of it? This actually pretty difficult. Some people think, "Oh, it could be a paper weight, maybe a planter, and oh, it could

be used to build a house, too, I guess." But to come up with 200 examples, it's not that easy. But if you ask an AI model, there's a good chance you

can come up with a long list of ideas. And your role, if you're actually trying to use a brick for

something, would be to evaluate these options to pick out which ones are the ones that you like. In brainstorming, a common guidance is the more ideas

the better. And so sometimes having AI generate a lot of ideas for you to pick from can be a powerful way to find one or two good ideas. So this is

the maybe more common use of AI as a brainstorming partner. I want to show you a different form of

brainstorming in which you give it more context and then also iterate with the AI longer, meaning have a longer back and forth conversation to help

get you to better options. So if you tell it, help me build a workout plan. I'm 38, bring the level, have 10 lb dumbbells in 15 minutes a day, then

they may give fairly generic answers like three workout plans. So, 10 squats, 10 push-ups, pretty

reasonable, very sensible, common sense answer. But if you want more creative options, giving it more context can be helpful. So, if you say, "I can't

stick to these. Give me hacks to stay on track. I have a trampoline and a cat." By encouraging it to give you trampoline and catreated workout

options, which is an unusual way of approaching workouts, it may ask you to consider trampoline breaks or

cat triggered micro workouts where maybe every time you see your cat wags tail or something, go do a tidy little workout. But these are certainly more

creative ideas. AI models have some inherent creativity because they've trained on a lot of texts on the internet which covers a lot of very different

ideas including some creative ones. And AI's output is a little bit random. So if you ask it

multiple times, help me build a workout plan, it'll probably give you slightly different answers. But if you give the AI basic questions, then common

sense relatively generic responses like do squats, push-ups, and so on are more likely. Let me plot a conceptual diagram where on the horizontal axis

I'm going to plot how unique a response is, how creative response is. So on the left were responses

like normal weightlifting exercises like bicep curls which is very common sense to then maybe slightly more unique things like standing on one leg

with a yoga block on your head to the really creative ones like cat triggered micro workouts. And on the vertical axis I'm going to plot the

probability of AI giving these different responses. And it turns out that it's much more likely to give a common

sense response than a highly unique creative response. There's a reason for this. Namely, it was trained on internet text. And there's a lot more

internet text talking about dumbbell curls than there are cat triggered micro workouts. And for most questions, this is actually okay because the

average information on the internet is probably decently factual. So when you are seeking information such

as what's the tallest building in the world is actually the Burj Khalifer. Most internet texts will say is the Burj Khalifa. There are smaller amounts

of text that will name other buildings but the average response and the most common response in the internet is usually the factual one for questions

like what's the tallest building? But if you're brainstorming, then giving the average information

of the most common response ends up with squats, push-ups, and almost never trampoline breaks and pretty much never catbased sessions. Which is why if

you ask the AI model to brainstorm with you, you get a lot of common sense ideas rather than the more creative ideas, which depending on your goal,

may or may not be what you want. So what do you want to do if you want to get high quality more

creative ideas from AI? We've seen with a basic prompt you get responses from the common sense space. But if you give the AI model more context, so

give your age, your level, but also tell it you have a mini trampoline, a cat, trouble staying motivated, nail squats, then this context pushes it

into the more relevant and creative space and it's more likely to give a custom answer rather than to

generate common sense answers. Now, one problem that you may face when brainstorming is if you're trying to come up with creative ideas, there's so

much context you could potentially give the AI model, what should you prioritize telling the AI model? It turns out there's a technique that is very

helpful for driving what context you decide to give the AI model, which is to iterate with the AI. Let

me show you what I mean. If I want to ask AI to help me brainstorm plans for paying off my debt, have $1,100 of credit card debt at 19% interest,

monthly minimum payments of $40, a student loan 8% interest, and a family loan $900. So, this gives decent background context. Then, one thing you

could do is ask AI not to give you one option or tell you what to do, but to give you multiple options to

choose from. I'll often ask you to give me three to five options. And so the AI may come up with a few different plans. Plan one is liquidity first to

preserve cash. Plan two is eliminate the highest interest loan. Plan three is prioritize paying your family back first. So these are all actually

reasonable ideas and I've not yet given it enough context to know which of these plans it should favor.

And it turns out that one of the really good ways to figure out what additional context to gifted AI is to give it feedback on the options it present

to you. Highly relevant feedback that allows it to then give you the next set of options. I don't like option one. It's too passive. I do like the

idea of paying off the 19% interest loan. Oh, and I forgot. I actually have $450 cash coming and I'm

also moving house soon. And then with this additional context, it now knows among plans one, two, and three maybe what you like and what you don't

like. And you can ask it to create three new plans. And then once again, by giving a feedback on these plans, you are giving additional context that

will help shape the AI models thinking. And you can keep on iterating like this for a while until it

comes up with a plan that you do like and then maybe have it flesh out the details of the one plan or two plans that you like the most. I found that

giving feedback to the AI on what it thinks like good ideas is just a very useful mechanism for very efficiently figuring out what's helpful context

to give to the AI. To summarize, if you're brainstorming, consider giving AI as much of the relevant

context as you can in advance and then ask it for a handful of options. Then give it feedback on the different options and ask it for more options and

iterate multiple times. Get more options, get feedback, get more options, get feedback. And do that a few times until you have one or more ideas that

you're satisfied with. If you follow this recipe for brainstorming, I think you find you get

consistently more useful and creative ideas. Now, you've heard me use the word context quite a few times. It's important to give your AI model the

right context so that it knows enough to do what you want it to. Let's take a look at the next video at how context works and how it is used to

produce a response. According to psychologists, most humans can keep only about seven things in their active

working memory at a time. That's why remembering a grocery list of about seven items is just barely doable if you aren't thinking about all the

things. But remembering a grocery list of 15 or 20 items is much harder. Interestingly, AI can use a large amount of context. Some models can have

context sizes of hundreds of thousands of words. Let's see how an AI model's context works and how you could

take advantage of it. AI models can read and reason over very large amounts of context. For example, if you are trying to choose an apartment, you can

upload hundreds of pages of lease contracts and upload tenant reviews and neighborhood statistics and ask AI to read all of this and to tell you the

pros and cons of each of the options. And you might write a prompt like pros and cons of each

apartment. Read everything and think really hard before answering. By the way, telling AI to think hard or think really hot is another common

prompting pattern that we'll come back to a little bit later. Context refers to all the text and files that the model uses to generate this custom

response to your query. If you give it a prompt like pros and cons of studying physics versus zoology, then it

will generate an output based on this very limited prompt, very limited context that you have given it. and the response will probably be fairly

generic. But if you give it more context, maybe give it your career assessment results and give it your high school schedule so it knows what class

you've been taking and then ask the same question. All this additional context will help it to give a much

more custom and likely higher quality response. If you're trying to think about what context to give to the AI model, think about what's all the

information that a trusted advisor would need in order to think at length and reason and then give you a good answer to your question. And a smart

adviser that knows nothing about you, but has just asked what are the pros and cons of study physics versus

zoology. The best it could really do is give a pretty generic response that's not custom to you because a context in the example in Tom just doesn't

have anything specific to you. AI models start with some amount of builtin context and leading AI models today can accept maybe up to around 750,000

words as context and this corresponds to about the first four or five Harry Potter books. So that's

lava text or several days of continuous speech. So many people underestimate how much information or how much context you can give to an AI model. Now

when you ask the AI model a question by default context is filled with a few things. First, there's something called a system prompt, which usually is

how the AI model knows what's the current date, knows the name of the model, basic capabilities,

maybe general instructions to be helpful to the user, and then if your AI model is able to use tools like a web search engine, in its context will

also be written descriptions of what are these tools and how to use them, such as what is a web search engine and how should it use a web search

engine. Before you've written your prompt, the context includes the system prompt and these two definitions.

And when you then write your prompt, your prompt is added to the AI models context. And it will then use all of these things as input to generate a

response. Like you have the options, full body, upper low, split, lower impact strength. The input text that is the prompts you've written as well as

the AI responses are called the chat history and the chat history gets incrementally added to the

context of the AI model as well. Now, if you start off giving the AI model more context, such as write a longer prompt as well as maybe upload a

handful of documents to tell it more about your workout schedule or your workout preferences, then all of these files can be included into the AI

model context and used to generate the response. Now if you continue the conversation to say I like this

about the first plan I don't like that about the third plan what you say here is added to the AI moral context and then this additional response in

this type of brainstorming workflow is further added to the context and this is why whenever you ask the AI to go back and forth and generate

additional answers it knows everything that's been said so far in the conversational history. Now let me take

this in a different direction. Imagine you had asked it for the workout plan like I shown here and it's given you a few workout plans. If you were to

instead give feedback on these plans go in a totally different direction and say now come with a workout plan for my mom. Well, a lot of the context

AI model has including your schedule, your workout preferences, all that isn't really relevant for

your mother's workout plan. I guess unless the two of you work out together. And so all this context would be distracting for the AI system and might

lead it to generate a worse answer. And in fact, it can be hard to know whether this answer was influenced by the previous context. This is why if

you're going to go off on the unrelated topic, it's better to start a new conversation so that you can

empty out the context and start with just the new prompt or just the information that's helpful context for the new question you want to answer.

You've seen how context is used to produce high quality responses. thinking about what's in the AI model's context and managing that so it has just

the relevant information and hopefully not too much irrelevant information although it can ignore a little

bit of it that will help you get better answers from your AI. One way of handling lots of context is to allow the AI model access to your computer so

that it can explore relevant files and pull in relevant files into the AI models context only as needed. Let's take a look in the next video at this

very powerful technique. AI is moving beyond just chat interfaces. You may have heard of applications

like cloud co-work or Microsoft copiloted co-work or Google anti-gravity. These are applications that can with your permission gather context

agentically from your computer. Meaning they can find and read files from your computer in order to give themselves the information they need to do a

task. This is a new and exciting way of using AI to accomplish real work. Let me illustrate a common use

case for these AI desktop apps. If you have been doing research on a topic and have a messy folder with lots of PDF, research reports, images, and so

on, you can with one of these apps ask it to read through the files in the folder and propose a new organization for it based on what it finds. In

this example, the AI is able to look through this folder and apply a bunch of changes, renaming files,

moving files around, creating subdirectories to come up with much more sensible organization folders. Let me show you the process of how is actually

done. You might start off asking it to organize a folder first to understand what's there and it can then automatically or agentically look at how

different files are named. After it has figured out enough of what are the files in this folder, it then

comes up with an initial proposal for how to reorganize it. And if you take a look at the initial proposal, you may be not fully satisfied and give it

a little bit further instructions to tell it what to do with this data. And finally, it comes up with a refined proposal, which I'm happy with. So,

I'm going to say go ahead and carry on these instructions.

and then it reorganizes my folder to make all the files much neater. Here's how AI desktop apps work. They are powered by an AI model. So, it comes

with the AI's pre-trained knowledge and it also has a set of tools like web search. So you can choose to carry out a web search if it needs to do so

to accomplish this task and has additional tools to work with files on your computer including the

ability to search through files to read files to write files to move and rename files and so on. So when asking a desktop app to do things in your

computer, a best practice workflow would be for you to tell what task you want done, such as organize the files in the folder. Let the AI system

propose an action plan, but not yet take action. You can then review the plan, give critique, maybe have it

update this plan if needed, and only when you're satisfied with it, then tell it to execute the plan, which you can go ahead and do on your computer.

One neat aspect of these desktop apps is that they can automatically explore files and manage contacts by reading files only when needed. If you are

using an AI chat, then you have to decide in advance what files to upload to give the AI model

context. So for example, if you need to write a schedule for filming, you might upload a file that outlines your filming procedures and based on that

it can generate a schedule. And notice that you had to decide in advance what context to provide to the AI. But with an AI desktop app, if you start

the application in say your document/forming folder, then if you tell it write a schedule for filming

this week, then the AI can decide to explore the files in this folder so that it can see what files are there, load the relevant files and then on

that basis figure out what is a good foaming schedule. And in this example, surprise, that actually noticed that one of your crew members birthday is

the week of filming. And so maybe you'll fold in a celebration for your crew mate, Mia. One note on

using these desktop apps safely. Desktop apps can get access to and can edit or even delete your files. And while mishaps of deleted files are pretty

rare, they have happened to people. So I encourage you to choose the most relevant folder to run the AI desktop app in. For example, instead of

running this in your whole folder and giving this access to all your files, maybe just give it access to

the subset of files it really needs for a task in a certain folder. When the AI system makes a permission request, I would encourage you to carefully

review the permission request to make sure you know what it is reading and writing. And so I'll give AI access only to the documents I wanted to know

about and let it write only to the files of places I wanted to. And when a AI desktop app deletes a

file, it often does not go to a recycle bin. So there may not be a way to recover the file. And if it edits a file, it behaves a bit differently than

if you were editing the document. And in particular, edited files usually don't have an edit history. And so it's not possible to go back if it made

some change that you may not like. So until you are very familiar with these tools, I'll encourage

you to look carefully at the permissions request it makes to decide what you do and do not want the AI system to be allowed to do. AI desktop

co-working apps are a powerful tool you can use to give an AI model the ability to discover relevant context as well as to take actions such as

reading, writing, moving, renaming files. Now, we've talked about context a lot in these last several videos, and

using as much relevant context as possible enables your AI system to help you with what's called reasoning toss. by which I mean tasks where you want

your AI to think maybe for a long time to give you the best possible answer. Let's go on to the next video to see examples of reasoning with AI. The

latest AI models have very strong reasoning capabilities. What that means is they can think

rigorously and at length about the task when given the right context. I find myself more and more using AI as a reasoning engine. Let's take a look.

Here's an example where thinking for a long time can help get a better response. If you are car shopping and are considering trade-offs among multiple

cars, you might upload spec sheets for the cars, insurance plans, column quotes, lots of documents,

and then ask what are the trade-offs for each car? read everything and think hard before answering. The AI model may then think for quite a long time

to read the documentation, maybe do some online search, then maybe think through what are the evaluation criteria that'll be right for you, and then

generate reports on the pros and cons of different cars. And just as doing research on what car to

buy could involve gathering a lot of information and thinking at some length about the pros and cons, AI can help you with that. As AI models get

better, their ability to carry out long running tasks has grown rapidly. This is a study by an organization meteor which plots for tasks at different

levels of difficulty as measured by how long it will take a human to do the task. That's the vertical

axis. How well can AI do these tasks. So for example, a task like finding a fact on the web may take a human just several seconds. Summarizing a few

vial texts may take a human an hour. Write a blog post couple hours. audit legal documents, explore a complex cyber security vulnerability, may take a

human many hours, and so on. And around 2024, 2025, models could start to do tasks that took seconds

to many seconds to many minutes or tens of minutes. And in 2025, models could start to have a decent success rate for doing tasks that to humans

longer and longer and longer to the point where now AI models can do tasks that can take humans many hours to do. Often AI model doesn't need 10 hours

to do a task that takes a human 10 hours to do, but also takes a bit longer than just a few seconds. And

this is what reasoning models has enabled for AI to think at great length in order to do these more complex tasks. If you remember the how many hours

in straw example, that's a task that takes a human just a few seconds to do. And several years ago, AI models used to sometimes get this wrong. And it

was also in that era, maybe 2033, 2034, that you may have heard advice like tell the AI model to

think step by step. And back then, this was good advice, but this advice is largely obsolete now. And I no longer tell my AI model to think step by

step. Instead, I'm more likely to just tell it to think hard. And it knows what that means, and that it should reason at length, not necessarily step

by step, but in even more complex ways in order to accomplish a task successfully. And so rather than

think about AI as counting ours and strawberries or having be told to think step by step, today you can ask AI models much more complex questions like

what are the trade-offs for each car or look at all this context and hope create a strip for a custom part. So for these more complex tasks, I

recommend trying to use one of the more modern models if you're able to access one. And there may well be

models even more modern than the ones listed on the slide that could be available to you now. So this is how AI reasoning or how AI thinking at length

works. If you ask it to plan the fastest way to visit five landmarks in Rome in one day, then it might want to gather quite a lot of information to

check map distances via web search, estimate walking times, searching hours, reorder the stops, and

so on, and then generate your optimized itinerary. The reasoning process may require the AI to think at length and repeatedly gather additional

information and then to think some more until it's satisfied with the answer. Conceptually, you can think of reasoning as a process like this. Given

your prompt in other input context, it will reason or think for a while using that context and then

depending on where it gets to, maybe it'll decide it's done and then just give you the final answer. Alternatively, after thinking for a while, it may

decide that it needs to use a tool to gather more information, maybe via web search or maybe by reading more files from your computer if it's a

desktop app, and then use that additional context to reason longer until it either again decides to use a

tool to gather more information or decides it's done. and so it can go through a few rounds of gathering more information and reasoning longer before

it decides the answer is good enough to present to you. If you are working on a complex toss and you want the model to think at length, then one way

to do so is to just tell the model to think. A few of the interfaces among the popular AI model

providers will have a thinking option. And if you select that option, that's a cue to the model that you wanted to think longer. Alternatively, in

your prompt, you can also just tell it to think really hard about this and it usually obey your instructions or some people will use the phrase ultra

think and that's another key word that the models understand as a cue to, you know, think really hard

about it. And if you do so, an AI model will sometimes think for many tens of seconds or even minutes or maybe sometimes even over 10 minutes in order

to give you a good answer. Especially with reasoning or with thinking models, I'd also encourage you to try giving the model hard tasks to see what it

can do for you. So if you are building a startup, maybe give it lots of context on what you're

doing and maybe tell it to design a topline plan for a fourperson startup with limited cash. I encourage you to give the AI model real job task, real

problems that you want to think through or that you want to solve. And as part of setting up for success, try to give it all the context that a human

expert would need in order to complete the task to make sure that the AI model has the sufficient

information that really anyone would need to do the task successfully. To wrap up, if you want to use AI to reason at length about complex problems, I

encourage you to use the best models available. The best models are often better than models that are maybe 6 to 12 months older. Remember to give it

as much context as is needed to carry out the task. It's okay to try giving a hard task to see what

it can do. So don't just give a trivial task. And lastly, either select thinking mode or just tell it in the prompt to think hard. We've spoken about

when it's helpful to use the cutting edge models, but it turns out even the most up-to-date models have some common issues. One being the tendency to

tell you whatever it thinks you want to hear. This is called psychopy. Let's go on to the next video

to see how to manage this behavior of AI models. AI models will act in ways to try to please you because of the way they've been trained. They have a

strong bias to tell you what you want to hear. This is called sick offensivey and avoiding it is a key prompting skill and involves prompting

neutrally and keeping context factual. Let's take a look at how to avoid sick offensive because this will

help you get much better answers from your AI. If you consider the pros and cons of remote work versus in office work and you ask it, don't you think

remote work is better than office work? This word in the question gives away what you're hoping the answer is and AI will probably say yes remote work

offers many advantages. In contrast, if you were to ask, is it true that office work is more

productive? Then it will probably agree with you that office work has these strong benefits. So depending on how you ask a question, it will tend to

reinforce your own preferences, your own biases, and this may not be the most helpful thing for you to make objective fact-based decisions. In a study

by the Washington Post on CHGB responses, it was much more likely to respond with phrases like

that's correct, good point, you're on the right track, compared to not quite right, that's not the case, or actually. And in fact, it tended to agree

strongly about 10 times more than it disagreed. And some models from Chai GPD have said things like, "Dude, you just said something deep without even

flinching. You're a thousand% right." And while some users appreciate AI agreeing with them like

this, I personally don't find it that useful to have AI just tend to agree with whatever I say even when I'm not right. While leading AI model

companies are working to reduce syphancy, it is still a problem. Models are trained to be hopeful assistants using human feedback and this reinforces

secrecy. For example, if you ask an AI model, I feel like it's better to be an introvert. Don't you? If the

AI model responds, that's an interesting idea. Here's why I tend to agree. Then most people are more likely to hit thumbs up on the feedback button

because, you know, it's a nice answer. Makes you feel good. But if AI were to say, "Not necessarily. Both types, introverts and extroverts, carry

tradeoffs. you know, people just don't feel as good about that answer and so they're less likely to hit

thumbs up or may even hit thumbs down. Because of this type of feedback, AI, which has been trained to generate more answers that leads to thumbs up,

positive feedback, will learn to try subtly to agree with people more often than not. And this leads to psychopensy. And secrecency feels hopeful but

actually degrades answer quality. Sometimes maybe is easier to spot. If you were to say, "I'm really

proud of this essay. What do you think?" Well, they'll probably agree with you, but other times it's harder to detect syreency. If you to say,

"Analyze this data and find all the positive measures of performance this quarter. you're subtly signaling that you're looking for positive measures

of the company's performance and so it's more likely to say something like data clearly shows revenue growth

strong intention improving margins and less likely to point out problems to avoid circ neutral framing of your questions and avoid giving any hints as

to what is the answer you want to hear so for example if I were to ask aren't carving taxes have for small businesses are really telling it what

answer I'm hoping for. In contrast, a more neutral prompt would be to what extent if at all do carbon

taxes affect small businesses. If someone reads the question on the right, it's actually not clear what answer they once is harder for AI model to be

sick of antantic. Or if you were to ask, do you agree that AI would create a lot of jobs? I actually happen to agree. But if I'm actually doing

research, I don't want it to just tell me what I want to hear. Instead, you can ask, "What does current

research say about AI's effect on jobs?" Or instead of asking, "Does remote work reduce worker productivity?" Maybe ask, "How does productivity

compare between remote and inoff work?" One common pattern is to lay out two options such as remote and in office work and just ask it for pros and

cons of the compare. but without hinting which of the two options I am hoping will come out ahead. So, don't

you think that was the best video ever? Oh, what do you think of the pros and cons of the videos you just saw? Sometimes it's nice to be told what you

want to hear, but generally that won't help you to do better work. Second, despite a lot of attempts to combat it, is still one of the pervasive

issues with practical AI usage today. And so implementing the strategies from this video, especially

taking more neutral framing, will help you get more objective and valuable feedback from AI models. Now, one of the most common tasks that people use

AI for is writing. Let's go on to the next video to see how to work with AI models to help with your writing. In a study by OpenAI, writing accounted

for 24% of tasks that people ask chat GPD to do. This is the single largest group of task. Writing

is really a kind of thinking. I find that when I'm writing, I have to think. And so AI, which is really good at thinking and reasoning, can help you

with this. But just asking AI to write for you often leads to AI slop or writing that sounds like AI writing which somehow feels different than human

thoughtful writing. Let's take a look at some techniques for getting AI to write effectively for you

and how to take advantage of AI reasoning and avoid AI slop. What makes AI slop? Many people have noticed that AI writing often includes the M dash,

that is the long dash, much more than normal human writing. On the social media site Blue Sky, the use of the N dash has been trending upward ever

since the release of GPD. A recent survey showed that 40% of US-based employees have recently received

work slop in the last month. And the term AI slop refers to content that's gened by AI and that looks good if you don't read it too carefully. So

maybe every sentence read in isolation sounds like it's well written, but collectively the text just lacks substance. It feels like it was written

without much deep or careful thought. One of the property of AI slop is it often contains sentences like

this. But it does change everything. is kind of vague, empty sounding, but also somehow overly important sounding text. AI's distinctive writing style

comes from certain words and patterns that it tends to overuse. AI tends to use fewer unique words and over represents some words and phrases. For

example, AI tends to overuse the words nuanced and delve. And it tends to use list of three more than

most people will. And it tends to use few nouns leading to phrases like this is a robustly structured and highly insightful paper. And this not X but

Y is not just about speed is about availability. In fact, I've been noticing on social media a lot more of this not X but Y type of verbiage. often

with X and Y both vague things like it's not about infrastructure it's about architecture and a lot of

phrases like those are just vague and don't reflect a deeply insightful point of view interestingly because humans are using AI models so much humans

are themselves starting to sound more like AI and humans ever since chat GPD was released are using the word delve more in podcast and talks. And this

is true both for spontaneous speech as well as for prepared speeches. So this isn't just a case of

people using AI to write scripts for them. It looks like people are picking up speech patterns or texting patterns from spending a lot of time with

AI. So what's a better way to write that avoids generating AI slop? One technique that I think you find helpful is to use progressive outlining in

which you don't ask AI to write the final text right away, but instead have it write an outline, refine

the outline, and iterate a few times before having it generate the final text. For example, here's a prompt that says, I'm writing an article about

small AI teams moving faster than large teams that don't use AI. And I just want to acknowledge that this is not a neutral prompt where I'm asking AI

to help me decide if small AI teams do move faster, but instead this is writing an article from a

certain point of view. But you can ask AI to research evidence for and against this hypothesis. So the AI model may search online and find a handful

of articles. Next, if you wanted to help you brainstorm a handful of options for the story outline, you can tell it to brainstorm or to create three

different outline options. Maybe tell it to include a counter argument section and also provide or

upload a handful of stories from AI team say that you work with. With this input context, the AI may give back a few different options for what the

outline for your article might look like. Option one could be to tell the three stories and then conclude with a thesis. Option two might explore

different patterns of how AI teams work and so on. Then following what you saw in how to brainstorm of AI,

you might give feedback on these options. And so you might say let's use option one and keep all the stories but move the thesis right after story

one. And also maybe say you want to add a historical analogy, Pixar treating the Toy Story in the 90s, which is actually a really inspiring story

where Pixar at that time a small company created Toy Story which was the first fully computer animated

featurelength film just using a really small team. Based on your feedback, the AI maybe gives you back a revised outline. If you're satisfied with

this outline, then you can tell it to expand each heading, not even into the final text, but just into bullet points. And following that, you might

decide to give it more feedback on the bullet points and iterate on the bullet points before finally

having to generate the text for the article. And it turns out that starting with an outline speeds up review. Let's say you're working on a fun

article about whether a flying squirrel can carry a coconut. In contrast to having AI first work with you on the outline, then on bullet points, and

then on the final text, maybe you ask the AI to write the final text right away, just from the start. If it

writes a sentence like this, you may be unhappy with a few words and you can edit a few words, but changing each word just changes one word and the

rest of the paragraph stays the same. In contrast, if you write an outline first and you're unhappy with part of the outline, then changing the

outline causes an entire section, that's a lot of words, of the final article to change. So that's why

editing the outline or iterating with the AI system on the outline is very high leverage because you can figure out how to change just a few words of

the outline and this will result in an entire paragraph or entire section if you find the article changing and this ends up being a much more

efficient way for you to think through what you want to say in an article and adapt it to what you want it

to be. Writing is one of the most common use cases of AI. According to open eyes data of the writing focused chats, about twothirds involves starting

from some pre-existing text rather than starting from scratch or starting from a empty sheet of paper. When you already have something written up, it

can be very helpful that AI critique it for you and help you make it better. Let's take a look at

some techniques for this in the next video. You often have some idea where you've already written some text about your idea, but want an AI model to

help you edit and refine your text. I often show my writing to AI to help me make it better. AI is great at this task and it always is time to read

your work, whereas finding a human to help you out might be trickier. You've already learned how to

avoid secrecy, but how do you get the best quality editing and critique? Let's take a look. One useful technique for editing with AI is to edit your

article piece by piece, such as one sentence at a time or one paragraph at a time, rather than telling it to edit the entire article all at once. And

to do a little brainstorming around each paragraph until I've nailed down one paragraph before going

on to the next one. For example, if someone's written the sentence, the public thinks achieving AGI, artificial general intelligence means computers

would be as smart as people. You might ask AI to help brainstorm a few different ways to say this. And so it may come up with a punchy way to say

this, a visionary way to say this, a conversational way to say this. And depending on your editing goals,

you could even iterate a little bit until you pick some version of rephrasing this part of the text that you like. After you've nailed this down, then

go on to the second sentence or maybe the second paragraph and work on that little bit with the AI and then go on to the next sentence and next

paragraph and so on until you get through your entire article. And I find that working on one piece of a

long article at a time makes for a much more manageable workflow than if it were to change a lot of things all at the same time and you're reading

this very long edited article to figure out what has changed and what you like and don't like. Now if you want highlevel more holistic feedback about

an entire piece you've written, it turns out AI can hope for that too. But because of psychopensy, AI

is often not a very good objective critic. For example, if you wrote a sci-fi short story about an astronaut stepping out of his ship, and if you ask

AI without further instructions to critique it, there's a good chance they'll tell you whatever you did is fantastic work. In contrast, there's a very

helpful technique to guide AI in how to evaluate your work to give you more helpful critical

feedback and that is to give it a rubric and that means a grading criteria. For example, you might write a rubric that specifies what are the most

important criteria by which to grade or to judge the work. So you may say that characters of the story is worth 25 points out of 100. The plot 25

points world building writing craft and establish a point system and then also develop detailed

instructions on how to evaluate each of these criteria. So to evaluate characters maybe ask if every name character has a go and that's worth 10

points. conflict between two characters goals and so on and giving AI very explicit criteria on how to judge work forces the AI to be more objective.

One thing to notice about these criteria is that each of them is very clearly and unambiguously defined.

So for each text each of these criteria is either true or false, yes or no. And there's nothing in between. So either it's true that every name

character has a goal or it's not true. And these completely objective criteria forces AI to look at whatever you're giving it through a objective well

specified standard with no ambiguity. And by the way, if you're not sure what rubric or what grading

criteria to use, you can brainstorm with AI to develop that rubric. And AI is actually pretty good at this, too. After you've written the rubric, you

can then provide the rubric as well as the story and in your prompt ask the AI to be objective. So critique to attach sci-fi store, assign a score per

category, then sum the scores at the end. And by giving the AI these very clear instructions on

what to do to sum the scores at the end, it then hopefully gives a more objective assessment of your story. If you want, you can also ask it to then

give you suggestions on how to improve the story to do better on this rubric and as a cause it to give you more focus suggestions to improve it in the

dimensions that you think matter the most. In contrast, poorly written rubrics encourage suency and

ambiguous or less objective thinking. For example, if you say, "I would work on the sci-fi story. Please score it out of 100." One of the things

that's strange about this prompt is you first ask it the score out of 100. So that will tend to cause it to leap to the conclusion about what score

and then only after that assign a score per category characters plot world building and writing craft. And

these are ambiguously defined categories because we're not told it how to score characters plot and so on. And so this will tend to cause the AI model

to first come up with some score and then justify it rather than score it carefully accordingly rubric and then add it up to then come with a more

thoughtful score. And as you see in the practice lab to come at the end of this module, this type of

rubric will tend to give higher scores than more objective rubrics. We talked about using AI to critique and help give suggestions for improving your

work. It turns out that having AI critique his own work or having one AI model critique a different AI model's work can also help improve the results.

For example, if you ask Chat GPT to write a user manual for a fancy role playing game, then it

might generate a file for you. And one thing you could do is provide a rubric for chat GPT to critique his own work. But a neat technique is to find a

different AI model, maybe Gemini, and give that a grading rubric to have Gemini critique Chat GP's work or vice versa. And it turns out that this type

of crossmodel review where you have one model review a different model's output, it helps

integrate a bit of knowledge from the two different models and can result in slightly better results than if you were to ask one model to critique its

own results. I think using multiple models in this context might give only a slight boost in performance. Realistically, if you ask Chachby to review

his own results or ask Gemini to review his own results, I think that will actually do just fine.

But sometimes I find it reassuring if a totally different model judges the output of a different AI model. And I use this technique only rarely

myself, but one thing I do is frequently switch between different AI models. It turns out that AI models are advancing rapidly and at different

moments in time, different models will do better on different tasks. And so routinely trying out different

models will help keep you sharp and keep holding your intuition about what model is best for what task. Air models have what's called jagged

intelligence. If the circle represents the task of what people can do, maybe in a job or maybe in a personal context, it turns out that AI can do some

things better than any person like quickly read tons of web pages or solve tricky math problems. But there

are also many tasks that AI doesn't do as well as people. So there's some tasks where AI does poorer than human and some where it does much better

than human and different AI models are jagged in different ways. So the task different AI models can do well are different. Moreover, the marketplace

of AI models is highly competitive. So CHB cloud Gemini really the long list of model providers are

releasing better models all the time. And so the best model for your tasks will likely change rapidly over time. And so I find that I'll often take

the same prompt and feed it to multiple different models to see how they compare. And this continuously holds my intuition about what models are best

for which of the tasks I care about. So that takes us to almost the end of this module. I hope you've

seen that AI models are really useful for reasoning tasks as well as for brainstorming, writing, editing, and critiquing your work. These are powerful

ways of using AI as a thought partner that I found very useful in my own work and that I'm confident you will too. Let's go on to the next video to

explore the practice lab for this module. I hope you practice the techniques we talked about for

using AI as a thought partner. In the upcoming practice lab, you can explore once again side by side more effective and less effective strategies for

both brainstorming and AI critique. Let's take a look once again. When the lab pops up, you can dismiss the tutorial. You can also find the tutorial

here as well as the instructions I hope you follow over here. And the buttons here, similar to the

previous lab, allow you to enter different prompts to brainstorm. Here's a prompt of less context. I need a workout plan, 30 years old, want to get

stronger. Whereas this is a more detailed prompt with more helpful context. And so you can run it like so and see the difference in the outwards. So

let me focus on this more detailed example on the right. It's given a program one which looks pretty

reasonable and a program two which also looks pretty reasonable. And we have also a few suggested prompts for how you might refine it. So maybe I'm

drawn to this. Let me do that. and have it continue the conversation. Then it will take this feedback on the programs it has given you in order to

refine this results. By the way, I actually use a workout program that AI had helped me to generate and I

found this helpful in my personal life of thinking through my regular workout plan. And so if you're a fan of working out, maybe you find some of the

suggestions it makes useful as well. Let's go back to the homepage. Here are a couple more examples of brainstorming prompts with a simple prompt. I

have $1,000 to invest. What should I do? Versus giving more context. And then what are some options I

should consider? Hope you try this out as well. As well as some examples of critiquing a sci-fi story as well as this is example of an objective

rubric on the right. And I hope you bring this up and read through it to give yourself a sense of what a well-written objective rubric looks like and

compare that with what a more subjective rubric looks like. And if you hit compare, you see the

difference in the quality of these reviews. And in fact, maybe not surprisingly, with the less objective rubric, it gets a 100, but a more objective

rubric of 75 out of 100. It also gives more helpful suggestions how to improve your story. In addition to critique and improving a sci-fi story, you

can also take a look at these examples of helping you improve a cover letter for applying for a job as

well as critique and help you to improve a possible business plan. After checking out these five examples, please also try your own prompts or try

your own stories or cover letters or business plans or something else. And I encourage you to use this interface to play with different ways to

brainstorm or to critique your writing. So that's it for this module. Please enjoy exploring the lab. Next,

let's go on to the final module where you see applications beyond text. Specifically, you look at multimodal prompting in which you get your AI to

also use images and audio and also look at building applications like games. It'll be a lot of fun and one of the examples we see will have something

to do with fireworks. So that I'll see you there.

In the previous two modules, we've not seen her AI generate text. But AI can produce richer types of outputs as well like images, videos and so on. We

call this multimodal outputs that is outputs with multiple modalities. Prompting for multimodal output is a bit different because multimodal

interactions are slower and more costly. But these capabilities will let you get a lot more done with AI.

Additionally, we'll also look at multimodal inputs specifically if you want to show some images to your AI and have it reason about that. Let's dive

in. AI models can generate images, videos, voices, even music, code, and more. You might have seen AI generate various fun, creative images. I want to

share with you one example of AI image generation that I really enjoyed. For my daughter Nova's 7th

birthday, I wanted a unique cake design, and she loves cats. So, we use AI image generation to explore different cake designs. The leftmost image here

is an AI generated image created with a AI generation software called Nano Banana which is created by Google and this is a picture that my daughter

really liked. She wanted a cake that looks like this generated image. We then took the image and

showed it to a baker and asked the baker to render this picture into a real life 3D cake. And the picture on the right shows my daughter cutting this

birthday cake that she loved. So in this case, image generation wound up being a brainstorming tool to explore different cake designs until we found

one that turned into a real life 3D cake that we all ate and liked. In addition to generate images, I

really enjoy playing with AI for video generation as well. Here's a fun video generated by our team of a man shrinking.

That video would previously have required maybe expensive special effects, but now AI can just generate it. AI can also generate voices. Here's a

voice clone of me reading out loud a letter from the batch, which is a weekly newsletter that deep learning.ai AI publishers to cover what matters in

AI. Dear friends, here's the latest from this week's issue of the badge. A barrier to faster progress in

generative AI is evaluations, evals, particularly of custom AI applications that generate free form text. By the way, I played audio of my voice clone

to both of my parents. And it turns out one of my parents could tell it wasn't me and one of my parents could not tell it was me or my voice clone.

And to avoid me get into trouble, I will not review which of my parents got it wrong. But I think

voice clones are getting really good. Lastly, AI can generate code. I mentioned my daughter loves cats. She also loves the color yellow. And so when

her teacher mentioned that she wish kids in the class could type or keyboard a little bit faster, I use AI to generate this typing game where if my

daughter hits the right letter, then she sees this fun little animation of a cat being fed, which she

loves. Using AI to write code has made it easier and more accessible for everyone, including you, if you wish, to write at least basic computer

programs. I'll say more about this later in this module. When you're working with AI model, there are many combinations you can use of input and

output types. For example, you can input text and images such as if you input an inspirational image like this,

if you like this Halloween costume and also the plan my Halloween costume. And in this case, the AI model might output text that says, "Let me help

you brainstorm a few alien inspired costume ideas." Or you can also upload music to an AI model and ask it help me plan my haunted house. And AI may

take these things as input and generate both text and a video of a haunted house design incorporating

your creepy sounds audio.

AI models can use most of these input types relatively easily. Some are slightly more expensive or slightly more costly to use as an input, but the

differences aren't very significant. In contrast, the time and cost of generating different types of output vary significantly. In particular, some

data types are much slower and much more costly to generate than others. To give you a sense, text tends

to be on the lower end in terms of time or cost of generation. So, AI is very efficient at generating text. In fact, modern AI has started with large

language models, sometimes abbreviated OM, but because they started with language, a lot of them were really adapted to deal with text. and it's very

efficient at that. Generating speech tends to be a bit more expensive and generating images even

more expensive and generating video much much more expensive than images or any of the other modalities. And the further we go to the right of this

chart, the longer it takes or the more time it takes to generate a single output and also the more costly it is to do so. Image generation has

progressed significantly in the last few years. Here's a short video generated by Imagen, which was in 2022 a

state-of-the-art model by Google, and it looks pretty good, but still has some artificial looking artifacts. The lines weren't quite right on the back

wall. The dishes changed midwash. In contrast, modern AI video generation looks much better and can also be synchronized automatically with generated

audio.

voice generation has also gotten much better. Here's what AI could do just a few years ago. >> But before you start tuning anything, you need to

define what success looks like. This step is easy to overlook, but it's foundational. >> It sounded a bit robotic, not that expressive. In contrast,

modern AI voice generation can sound much more expressive and much more natural. >> But before you start

tuning anything, you need to define what success looks like. This step is easy to overlook, but it's foundational. If you're generating multimodel

data, some of the techniques you learned earlier, such as giving the model enough context and maybe using the best model available, those are

relatively easy techniques to apply as well to multimodel generation. But some of the other techniques like

generating multiple options, you can still do that. But if each option now takes many seconds or even a few minutes to generate, then this becomes

harder to apply because you end up having to wait for a long time. Or if you want to iterate through many designs, then that too becomes harder if

each generation takes a long time or as costly. But if you have the patience to wait a little bit longer,

then all of those techniques also apply to generating audio, images, video, and so on. With great power comes great responsibilities. And AI

technologies can be used for good or for harm. Take voice generation. If you have recorded a podcast and you want to make little fixes, that can be

quite conveniently done today using AI voice generation to just reynthesize one or two words that you may have

flubbed. Or if you're building a video game and you want to give characters lifelike voices, more and more video game designers are using AI voice

generation to do so. It does raise important questions about the livelihoods of voice actors and I sympathize with all the voice actors that are

worried about AI voice generation. At the same time, I think it is also very valuable that AI voice

generation is making it easier for a lot more people to build entertaining video games, including developers that don't have access to the great voice

actors. In contrast to these applications, there are also some that are clearly harmful. Unfortunately, there's been a rise of scams where someone

would use a AI voice clone to pretend to be someone else, to maybe pretend that someone's relative is

an emergency and to ask to wire emergency funds. The number of beneficial use cases of AI vastly outnumbers the number of harmful ones, but we still

have work to do to combat the harmful applications. And I hope that each of us will only use these techniques for beneficial and responsible

applications. So, as you've seen, AI can now work with much more than text. It can work with images, audio,

video, code, and more. And most of prompt techniques you learn. so far will be helpful for handling multimodal inputs and outputs. One especially

useful capability is giving AI images as input so they can see what you're talking about. Let's go on to the next video to see how to use images in

your prompts. Providing images of your prompt can enrich the context for the AI. pictures of something you

want the AI to see, pictures of handwritten text, really anything that might be hard to describe in words. This video will help you build intuition

about what AI can see in images. Here's a picture of me explaining some concepts in AI in front of a whiteboard. My handrinting is not that great and

there are a number of math concepts that I'm trying to illustrate on this whiteboard. If you upload

this picture to an AI model and ask what is this class about, it may output something like this. He's teaching a convolutional neuronet network. And

the neat thing is my head is blocking the word convolutional, but it knows from this picture that I'm teaching about a specific AI technique called a

convolutional neuronet network. and has extracted some facts about what I'm drawing and also has some

good guesses about what I might ask students to do next. So is able to make a pretty smart interpretation of this image. One weakness of AI models in

terms of how they look at images is they tend to look at the coarse image but may miss fine grain details. So for example, if you upload this picture

to an AI model and ask what are these machines at my gym, it may confidently give an answer like

this, which turns out to be wrong. And that's because a lot of gym machines, if you look at them through a slightly blurry lens, they all look a

little bit similar. And AI is not that good today at look at the fine details of images to distinguish what really is a glute kickback machine or a

hamstring curl machine. In contrast, if you were to upload an image like this and ask it to create a sales

ad for this item, it actually does pretty well because this is very visually distinct object. And so if you're looking at this even through a slightly

blurry lens, yeah, you kind of see this is a humansized hamster wheel treadmill. When you upload an image, you can also give it moderately complex

instructions on what to do with it. So uploading a receipt like this, you can ask it, "What's my

portion of the bill? I had these items." And in this case, it gets it correct. AI's ability to read text like this is not bad. It does make mistakes.

So, I wouldn't trust it for high stakes applications, but if you wanted to take a quick look and if you're willing to spend a few seconds to double

check the result, then it could do decently well. An AI turns out to be pretty good at even reading

handwritten text. If you upload a picture like this to AI and ask it to transcribe it, it does a pretty decent job. Feel free if you want to try

reading this cursive handwriting yourself to see if you can outperform the AI. And so if you upload an image like this and write a prompt like build

an archive of a family's history based on these handwritten letters, I wouldn't trust it to read

everything completely accurately. But it might take a reasonable stab at this task. Rather than uploading a single image to AI, sometimes you can

upload many images. For example, if you just had a brainstorming session and had some notes you taken as well as pictures of post-it notes and

whiteboards, you can upload pictures and notes to an AI model and ask it to summarize the ideas from today's

brainstorming meeting. And again, it will probably do a decent job interpreting these images to come up with some summary. Probably not perfect, so

it's worth double-checking his output, but this could help you accelerate coming up with notes from today's meeting. To recap, AI models can read

basic text in images. Visual understanding, however, may miss details in the image because it tends to see

the image in a pretty coarse way. And you can also use many images when needed to give the AI more context. A picture is worth a thousand words. So

adding an image to a prong can often be the fastest way to get the AI model the best context. Like taking pictures of a brainstorming exercise or

digitizing your grandmother's handwritten recipe book. Of course, AI models can also generate images. This

is an interesting capability because it works somewhat differently to how AI models generate text. Let's go on to the next video to see what are some

fun images you can generate. Generating images of AI has made my life more fun. For example, I've used image generation for my kids birthday parties

or to make fun illustrations. Generating high quality AI images is a skill you can learn. And

understanding how AI image generators were trained will help you to control image generation to get better outputs. Let's take a look. One neat

application of AI image generation is if you input an image and ask it to edit it. This is a childhood picture of me on the right, my younger brother

on the left, and one of our childhood friends in the middle. And this is a old somewhat faded image. And

if you upload this to AI model and ask it to remove the glare and the rough texture and to make it a more natural aspect ratio, it can produce

something like this, which looks like a nicely restored photo. This particular image restoration was done with Google's nano banana model. If you're

not sure how to prompt an AI image generation system, you can actually ask a textbased AI model to help you

write a prompt. For example, if you ask it, generate a prompt for an image of a cat secretly running a coffee shop at night. An AI text model may

write a prompt like this. Notice that here it specifies a setting, specifies details of the character, and specifies a mood or a style. And if you

don't like any of these details, you can modify them to your liking. And a prompt like this might generate

this cute picture on the right. People skilled in the visual arts have a certain language for describing images. For example, a picture like this with

this look is cinematic. This is a watercolor image. This is a cyberpunk image. And this is an anime image. And I find that art buffs and art history

buffs excel at image prompting because they understand the language of images and can describe what

they want using more precise language than those of us that don't know this language and can't quite find the right words to describe the look that we

want. So if you want to become really expert at generating images, it could be worth reading up or studying a little bit about the language of images

to understand how to accurately describe different images. And in fact, one way to do so would be

to upload images to an AI model and ask the AI how it would describe those images.

And this could whole new instincts on what types of words can be used to describe what types of images. It turns out image generation uses a very

different technology than text generation. When AI is generating text, it produces the output piece by piece or it generates a few characters at a

time. In contrast, when generating an image, it doesn't generate the image a few pixels at a time. It

generates the entire image all at once. Specifically during training, that is when an AI model is looking at pictures maybe found online. In order to

learn what images look like, it will typically look at captions or descriptions of images like a small potted plant on a wooden table. And it'll learn

to start from image that looks like pure noise. This is just a grid of random pixel values. And it

will then learn to sequentially remove or subtract noise from the image to go from pure noise on the right to a slightly blurry picture of a potted

plant to a less blurry one to a less blurry one to finally to a sharp picture of a potted plant. And that's what AI model tries to repeatedly practice

doing during training. A model that does this is called a diffusion model. Then when you come in and

when you write a prompt, maybe create an image of a potted plant pond in the table, it then goes through this process starting from a pure noise image

and then gradually tries to remove noise from it to come up with that final image. And the key is how it learns to subtract noise to maybe reveal the

image that the person might have had in mind. Diffusion models do generate random outputs and they

can also make certain types of errors. If you repeatedly ask AI model to generate a positive plant, different times you run the algorithm might result

in different images like the one shown here. But many people have also observed that the diffusion model tends to generate weird looking hands, often

with more or fewer than five fingers. And it can often output gobbled text like happy birthday is

very badly misspelled here. And it can also lead to inconsistent characters. So if you ask it to generate a cartoon, the character's hair has changed

between the two frames of this cartoon. Fortunately, modern AI models have become much better at addressing these problems. For example, modern models

like Nano Banana can allow you to upload a number of research papers and ask it to generate an

infographic and it'll do a decent job with text that looks mostly plausible. Or if you ask it to generate cartoon, the more modern models can generate

fairly consistent characters. Meaning now this character, as you can see, looks very similar from frame to frame of this cartoon. And the text also

looks pretty decent. Compared to text generation, image generation can be slow and costly. For

example, if you're generating just a short paragraph of text, many eye models could do that in just seconds and it may cost less than a scent. Of

course, generating long paragraphs of text or if you ask it to think for a long time, that can cost more. And also, AI models were generated word by

word or maybe a few characters at a time and you can interrupt it or have it stop early if you want. In

contrast, generating a single image might take tens of seconds and cost many sense and it generates the image all at once and there often isn't an

option to stop early because image generation is much more expensive. That's why our ability to iterate with images is usually more limited. And if

you're generating videos, then it gets even harder. Even though some things are more expensive to

generate, the good news is the cost of generating virtually anything with AI is trending downward. So a year from now, it will be less expensive for

you to create art for your home or graphics for a family member's birthday card compared to today. Beyond generating images, AI can also help you

create fun games and websites without having to write any code yourself. This is a more advanced

capability and it's so easy to get stuff that doesn't work or for noviceses to get stuck trying to build more complex applications. But I want to just

give you a taste of how using AI to build custom software works. While it's not that easy, it's also certainly much easier than it was just months ago

and quite likely easier than you might think. Let's go on to the next video to see how to use AI

to create your own mini game or website. Building computer games and websites used to be something that only professional developers were able to do.

But the ability to do this is being democratized. By writing text prompt, you too will be able to build basic software applications and websites. This

does take some skill and I don't want to make it sound trivial, but in just this one video you

learn some of the basics. This is a very exciting capability and I encourage you to explore it. I've seen many people who are not software engineers

have a lot of fun with this and even with just one prompt is often possible to get a cool little game or app. Let's see some examples using this

prompt. Build a game where the user has to place obstacles and they go and it creates a simulation of what

you design. Claude created this game which you can play and is, you know, actually pretty cool. So just a short simple prompt like that, a leading AI

model can create a reasonably interesting game. One example that you see in the practice lab is prompting an AI model to generate a fireworks display.

Here's a prompt and this creates this app which is actually pretty fun to play with.

If you're trying to write a prompt to tell AI to build a simple app for you, here are some building blocks you might consider including in your

prompt. First is to specify your goal for what you want to create. Next, specify what are the inputs, what the users need to input into the system.

And then lastly, what are the outputs or what the app shows back to the user. For example, in this prompt,

we are telling it that the goal is to generate a fun fireworks simulator. The input is I want to do a click on the screen and the output is see a

colorful display of fireworks. Sophisticated developers will use AI to build much more complex applications than what I'm showing here. But just to

continue with the simpler examples, to build a game like this, you might ask it to create a fun game where

the user has to place obstacles and a go and it creates a simulation of what you design. In addition to entertaining games, you can also make more

useful and functional apps to help you save time or make your life a bit easier. For example, you can create a work timer called a pomodoro timer.

This is a type of timer that people use to time their work or studying with 25 minutes of work

interference with a 5minut break. Or maybe you can create a bill calculator. Here you can input the bill and the number of friends you need to split

the bill with and the app can tell you how much each person should pay. or maybe build an outfit picker app that helps you decide what to wear based

on the weather. Each of these apps could be fun or useful in everyday life. And they're also good

starting points if you're new to making apps with AI because they're quite simple. In particular, they each have a specific well- definfined task.

They also don't need any additional files that need to be uploaded or outside information. And they're also something you can open up, use for a short

period of time, and enclose. If you're curious to try using AI this way yourself, I encourage you to

experiment starting with building simple apps. It turns out some ideas are easier to create than others. For example, a simple platformer game would

be relatively easier to build, or a quiz to practice French words. In contrast, a multiplayer game played over the internet that would be harder and

much more complex or live French practice with AI feedback would also be harder to build. It takes a

while to hone intuition about what is easy for AI to build and what is hard for AI to build. If you're not sure, I encourage you to just try it out.

And the worst thing that could happen is it doesn't work and you start to hone your intuitions about what's hard. But if you're just getting started

trying to use AI to build apps, I encourage you to start with simple ideas like build a simple game

and see if you can get AI to do that. When we get to the practice lab in this module, you'll be able to try a few examples that you can build with

just a single prompt. If you want to dive deeper into using AI to build software, I encourage you to take the course build of Andrew offered by deep

learning.ai. In addition to writing code to build games and websites, AI can also help you to analyze

data, which it can do by writing code to do that data analysis. Again, you don't need to write any code yourself. Just tell AI what you want and or

try to write code to do it for you and this can lead to hopeful insights in your work and personal life. In the next video, let's take a look at using

AI for data analysis. If you have data from your personal health records, like maybe if you use one

of the apps that track your heart rate or running time, or if you have data of sales records from your company, or really most other types of data,

like what you might store in a spreadsheet table, AI can be pretty good at writing code to analyze this data for you and to try to extract useful

insights. Let's look at some examples that I hope will inspire you to try out this capability. If you have

ramming tracker data that you can download into a spreadsheet, you might try uploading that spreadsheet to AI model and asking it how are my pace and

distance progressing and an AI system might spend some time to write some code to analyze it and maybe generate a plot for you and also potentially

provide some insights. Or if you run a small business and you have a spreadsheet of sales data, you

can upload that data and ask the AI model, what if you tell me about this month's sales? The AI could analyze for a little while and it might actually

write some code to do things like compute the monthly revenue or to create a graph and try to show you whatever insights it finds. I find that AI data

analysis often isn't as sophisticated as a really good human data scientist. But for pulling out

basic insights and doing so efficiently, an AI model can be pretty good. How do AI models write and execute code? It turns out that the way it works

under the hood, the ability to write and execute or to run code is like any other tool that the AI model might use. You've seen how AI model might

have tools to carry out web search or to read and write files and so on. And some AI models also have a

tool to run a computer program or to run code. And this capability will often be used when data is present or when some sort of calculation or some

sort of plotting of a graph is needed. In that case, the AI could generate a bunch of code and then use this tool to run the code in order to generate

a result for the user. We saw previously how a reasoning model might input a user prompt, reason for

a while using the context, and occasionally decide whether or not it needs to use a web search tool to get more context. In this case, it has the

option of not just using a web search tool, but also a run code or code execution tool in which the AI can write the basic computer program to analyze

data, compute averages, plot a graph, or whatever is needed in order to get to the final answer. Let's

look at a concrete example. If you're running a bubble tea shop, you may want to look at your sales trends over time to answer questions like, "Did

your new drinks sell well compared to existing drinks." If you have sales data handy, AI can help you with this. You can attach your sales data as a

file and write a prompt like this one. Which drinks had the biggest changes in sales? Graph it. The AI

will then go through an agentic process to analyze the data and graph it. Inspect the data, then calculate the monthly changes in sales, and do things

like analyze intelligently your data to help you get useful insights. For example, it may say things like, I'm noticing some clear patterns. Most

drinks are flat, but four stand out. Now, graph those. So, not just graphing all the drinks, but

identifying and potentially focusing on the most interesting ones. Then, it might generate a graph like this one. Time is on the horizontal axis and

number of sales on the vertical axis with each drink drafted in its own color. So the strawberry matcha took off in spring, mango green tea and

strawberry lemonade in summer and your new coconut milk tea did well in fall. It also added these colorful

highlights to make these trends stand out. So in just a few minutes you can get a highly useful graph like this. From this graph, you might conclude

that your spring promotion for strawberry matcher was strong and maybe you want to try that again next year. You can keep going and iterate on this

graph and ask for different insights or changes to the graph or give more context about your business

to the AI to give it a better shot at finding more useful insights. But let me show you a more longinking example. You could take your sales analysis

further and ask for a more comprehensive graphic. For example, you might want to create a year in review graphic for your business to present to your

team. This involves analyzing all your data in a few different ways to find out most interesting to

share. So you might write a prompt like create a one slide year and review graphic for a bubble tea shop. Analyze the data carefully for insights and

again attach your data file. Using the words carefully in your prompt may trigger the AI to think for several minutes to complete this analysis. And

it might go through an agentic thinking process like you saw on the previous slide. And it will

likely write and run code to calculate things like revenue and items sold. And in the end, you might get a graphic like this with a lot of interesting

insights like brown sugar and classic being the most ordered drinks and most customers choosing a large drink. This graphic also has a creative bubble

tea color scheme. You probably want to double check these figures to make sure they match your

expectations since the AI sometimes can hallucinate, but you can get a good analysis in a relatively short period of time just by prompting. that's

fairly likely to be accurate since AI calculated these numbers by writing and running code. I encourage you to try this type of analysis yourself if

you have data like sales data or personal data you're interested in getting insights from. If you're

using an AI model that can run code, when would it choose to do so? We've seen that for some questions it can use this pre-trained knowledge. If

you're asking for a question that can be answered using common knowledge on the internet, what it already knows may well be good enough. If you're

asking a specific question that is real time, then web search enable AI will help you get a better answer.

You may want to ask her to use a deep researcher if you have a more complex question that may require multiple related searches such as to come up

with a complete plan for your Halloween house. And for queries that require calculation or drafting, those are the types of questions where it's most

likely to write and run some code in order to carry out that task precisely for you. One of the most

powerful things that AI can do now is write and run code in order to carry the task for you. If you have some data that you want to get insights from,

I encourage you to try exploring it with an AI model. AI isn't always reliable and it's better at simple analysis than really complex ones. So

consider double-checking this conclusions, but it can be much faster than having to do the analysis

yourself. an Excel or Google Sheets and it has on many occasions helped me discover useful insights in my data. To put your skills into practice, we

have one last practice lab and an optional final project for you. Let's go on to the next video to see what these are. I'm excited for you to test out

building games and applications using AI. This should give you a taste for what's possible to build

with AI. When you get into the lab, you can read through these instructions shown here, but I'm just going to dismiss this for now. And I'm just going

to click the prompt for a fireworks show. I encourage you to read through this prompt carefully so that you understand what it takes to build an app

like this. And I'm just going to hit run. And this short prompt will build this application. I did

leave a lot of things unspecified, but it looks like my AI has made reasonable decisions. And if I click my mouse, it launches pretty fireworks. If I

don't want to launch them by myself, the auto show runs my automatic fireworks show. And then here's the grand finale. So, pretty cool. One neat

feature is that you can actually share the apps you built. So, I'm going to click share. Just copy the

link to my clipboard. And I can open up a new tab with this URL. And this actually launches the Fireworks app in my web browser. So, if you share this

URL with a friend, they'll be able to run the Fireworks app that you just created. One of the things I most enjoy about building simple applications

is to share it with friends. So, I encourage you to maybe take what we built and consider sharing

that with friends and see what they think. Feel free to take the firework show prompt modified and see if you can get a different result that could be

even more to your liking. Second example, let me click on the color palette prompt. If you are designing a website, something you may have to do is

choose a color palette for the website. And so with a prompt like this, you can build a color palette

picker. Here's a base color in RGB values. So this is actually roughly the color of my shirt and you can choose complimentary analogous and so on

color palettes that you know they actually go pretty well together. Hope you have fun playing with this. So, I hope you try out all four of these

built-in prompts and additionally try using your own prompt. For example, here I'm going to ask it to build

a fast card app to help me practice basic French vocabulary. This process may take a few seconds and when it's done, this is what you might get. Where

what is yes? Not too shabby. And so on. Looks like I got that right. It looks like it g me all the answers here on the right. But if I don't want

that, I can go back and modify the prompt to redesign my flash card app. Please have fun with this. And

I find it inspiring that with just a one English prompt. You can build a web page to build applications like these. The final project is to build a

simple app from research on a topic that interests you. We're going to go through three steps. First brainstorm a research question, then run

research, and then to build an app. I encourage you to read through these instructions, but I'm just going to

dismiss them for this demo. The first step is to brainstorm a research question on either a topic you want to explore or a decision you want to make,

like researching health supplements or buying a car or things to do your career or some fun things with astronomy. Let me pick the careers one. Given

this prompt or some other one that you may choose, I would encourage you to then give the AI more

context. So I miss exploring different careers about me, my situation. Let's say I'm at university studying deep learning, want to work on office and

I encourage you to write more than I am here in this demo walk through. But let me send it to the AI. And here it will help me to brainstorm a few

specific research questions such as what careers let me spend a lot of time collaborating and what does

it look like dayto-day? So hopefully you pick something that's relevant to your life and this will help you brainstorm a range of different questions

that hopefully will be interesting to you. And we also have a AI mentor to give you some feedback on how the brainstorming process is going. So in

this brainstorming workflow, I would read through these questions and give feedback. So let's say I'm

most interested in question one, but like the specificity of question three. I'm also want to make sure I have time to go to my job. So this type of

feedback, the AI now has additional context about what make an interesting research question for you. And so it refineses it to slightly better

options. Let's take one more turn. Based on these three questions, my feedback is and I also want to make

sure to consider nonprofit work. And so by going a few rounds with looking at the brainstorm research questions and giving feedback, we've now refined

it to a handful of research questions. So if you're using an OM, you can actually go for multiple rounds, ask you to refine the questions, combine

them. But let me just end this part of the exercise for now and say I like this question the best. And

I'm just going to pick this question to go on to set two now that I've formulated a research question. And what we just did was go through a

brainstorming exercise. There's an example of how you may iterate with your AI to brainstorm any of other possible topics as well, not just to

identify a research question to work on. Now that I've picked my research question, let me give it a little bit more

context. So, I also want to know about salaries and typical compensation. And I wanted to use these sources forums for personal experience and so on.

I encourage you to add more personal context and add more sources that I'm doing in this quick run through. But let's just have it carry out this

research. And let's have it go ahead and do so. The mentor gives some feedback on my prompts. And after

running for a while here, my web search enabled AI gives a pretty decent set of results with lots of citations. For the third and final step, after

you've gotten your research report, let's go build an app. We can build a quiz to test my knowledge based on the reports or does a mini game or build

an infographic. All three of these are actually pretty fun, but I'm going to choose the quiz option.

So, here I'm going to build a five question multiplechoice quiz. And the research report that we had created from step two is uploaded here as an

attachment to give it more context. And to keep the results more predictable in our website, this prompt is grayed out. So, it's not editable, but you

can always copy the report into a third-party AI system such as Chadly or Gemini or Claude to

experiment more. And I encourage you to play of all of these. All of these are actually pretty fun. But let me just generate my quiz app. So, that

will again take a little bit of time. And so, here's the app. Let me pick that. Yep, looks like I got that right. And so on. And same as before, you

can copy this link using the share button and share this app with your friends and see if they like it.

So, I hope you enjoy going through the iterative brainstorming workflow, which is a very useful skill to have when using AI and then provide enough

context for it to do research for you and then based on or inspired by that research, build one of the fun apps that we just showed you. and maybe

even share it with some friends. I hope you enjoy exploring the lab and the optional final project and I

hope you'll share your final project with others as well. And again, if you're interested in learning more, I encourage you to take the Build of

Andrew course. Once you're done, I'll see you in one final video.

Congratulations on making it to the end of this course. With all the techniques you've learned, you're now ready to be an AI power user. I hope you

find a lot of places in your personal life and work where these skills will benefit you. like using AI to help brainstorm or use deep researcher where

you need thoroughly researched reports or use AI to help you with writing and even generate

multimodel outputs and code and even as you're doing all this AI models will keep on getting better. So please keep trying new models and give AI hard

tasks and provide highquality context to help you to keep honing your intuitions about what AI can and cannot do. I'm confident you get a lot out of

this incredibly powerful technology.

Thank you for sticking with me to this point and I hope you use these powers to help yourself, your friends, your community and go make the world a better place for yourself and others.