jonelepord

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 her 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 me. 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 LLMs, large language models, as a developer tool, that is using API calls to LLMs 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 LLM 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 us 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 us with what is the capital of France, then based on what articles on the internet might have, it's quite possible that the base LLM 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 LLM, which is where a lot of momentum of LLM research and practice has been going, an instruction-tuned LLM has been trained to follow instructions. So, if you were to ask it what is the capital of France, it's 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 LLM that's been trained on a huge amount of text data and further train it, further 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 are less likely to output problematic text such as toxic outputs compared to base LLM, 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 LLM, 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 Isa and I will be presenting. I'm very grateful to Andrew Mayne, Joe Palermo, Boris Power, Ted Sanders, and Lillian Weng from OpenAI that 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 DeepLearning side for the work of Geoff Lodwig, Eddy Shyu and Tommy Nelson. So, when you use an instruction-tuned LLM, think of giving instructions to another person, say someone that's smart but doesn't know the specifics of your task. So, when an LLM 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 helps the LLM 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 LLMs. And you also learn from Isa a second principle of prompting that is giving the LLM time to think. So with that, let's go on to the next video.

know this time