Welcome to this course on ChatGPT prompt engineering

for developers. I’m thrilled to have with

me Isa Fulford to teach this along with me. She

is a member of the technical staff of

OpenAI and had built the popular ChatGPT

retrieval plugin and a large part of the work has been teaching

people how to use LLM or large language

model technology in products. She’s also contributed to the

OpenAI cookbook that teaches people prompting. So thrilled

to have you with you. And I’m thrilled to be here and share

some prompting best practices with you all.

So there’s been a lot of material on the internet

for prompting with articles like 30 prompts everyone

has to know A lot of that has been focused on the

ChatGPT web user interface Which many people

are using to do specific and often one-off tasks

But I think the power of LLM large language models as a

developer to that is using API calls to LLM To quickly build

software applications. I think that is still very

underappreciated In fact, my team at AI Fund, which is a sister company

to DeepLearning.AI Has been working with many startups on applying

these technologies to many different applications

And it’s been exciting to see what LLM APIs

can enable developers to very quickly build So

in this course, we’ll share with you some

of the possibilities for what you can do As well

as best practices for how you can do them There’s

a lot of material to cover. First you’ll learn some prompting best

practices for software development Then we’ll cover some

common use cases, summarizing, inferring, transforming, expanding And then you’ll build

a chatbot using

an LLM We hope that this will spark your imagination about new

applications that you can build So in the

development of large language models or LLMs, there

have been broadly two types of LLMs Which

I’m going to refer to as base LLMs and instruction

tuned LLMs So base OMS has been trained to predict the next

word based on text training data Often trained

on a large amount of data from the

internet and other sources To figure out what’s

the next most likely word to follow So for example,

if you were to prompt this once upon a time there

was a unicorn It may complete this, that

is it may predict the next several words are That live in a magical

forest with all unicorn friends

But if you were to prompt this with what is the capital

of France Then based on what articles on

the internet might have It’s quite possible that a

base LLMs will complete this with What is France’s largest

city, what is France’s population and so on Because articles on the

internet could quite plausibly be lists

of quiz questions about the country of France

In contrast, an instruction tuned LLMs,

which is where a lot of momentum of LLMs research and practice

has been going An instruction tuned LLMs has

been trained to follow instructions So if you

were to ask it, what is the capital of France is much more

likely to output something like the capital of France is Paris So

the way that instruction tuned LLMs are typically trained is You start

off with a base LLMs that’s been trained on a huge amount

of text data And further train it for the fine tune it

with inputs and outputs that are instructions and good

attempts to follow those instructions And

then often further refine using a technique called RLHF

reinforcement learning from human feedback To make the system

better able to be helpful and follow instructions Because

instruction tuned LLMs have been trained to be helpful, honest

and harmless So for example, they’re less likely to output

problematic text such as toxic outputs compared to base LLMs A lot

of the practical usage scenarios have been shifting toward

instruction tuned LLMs Some of the best practices you

find on the internet may be more suited for a base LLMs

But for most practical applications today, we would

recommend most people instead focus on

instruction tuned LLMs Which are easier to use and

also because of the work of OpenAI and other LLM companies becoming

safer and more aligned

So this course will focus on best practices for

instruction tuned LLMs Which is what we recommend you use for most

of your applications Before moving on, I just want

to acknowledge the team from OpenAI and DeepLearning.ai

that had contributed to the materials That Izzy

and I will be presenting. I’m very grateful to Andrew Main, Joe Palermo,

Boris Power, Ted Sanders, and Lillian Weng from OpenAI

They were very involved with us brainstorming materials, vetting the

materials to put together the curriculum for this short

course And I’m also grateful on the deep learning

side for the work of Geoff Ladwig, Eddy Shyu, and

Tommy Nelson So when you use an instruction tuned LLMs, think of giving

instructions to another person Say someone

that’s smart but doesn’t know the specifics of

your task So when an LLMs doesn’t work, sometimes it’s because the instructions weren’t

clear enough For example, if you were

to say, please write me something about Alan Turing Well,

in addition to that, it can be helpful

to be clear about whether you want the text to focus on

his scientific work Or his personal life or

his role in history or something else And

if you specify what you want the tone

of the text to be, should it take on the tone like a

professional journalist would write? Or is it more of a casual note

that you dash off to a friend that hopes the OMS generate what you want? And

of course, if you picture yourself asking, say, a fresh

college graduate to carry out this task for you If

you can even specify what snippets of text they should read in

advance to write this text about Alan Turing

Then that even better sets up that fresh

college grad for success to carry out this

task for you So in the next video, you see examples of

how to be clear and specific, which is an

important principle of prompting OMS And you also learn

from either a second principle of prompting that

is giving LLM time to think So with

that, let’s go on to the next video