跟读练习: Prompt engineering - Introduction - 通过视频学习英语口语

Welcome to this course on ChatGPT problem engineering for developers.

I'm thrilled to have with me Iza 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 work has been teaching people how to use OEM 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.

I'm not sure.

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.

I think the power of LOM's large language models as a developer too, that is using API calls to LOMs 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 deep learning AI,

has been working with many startups on applying these technologies to many different applications.

It's been exciting to see what LOM 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 certain prompting best practices for software development.

Then we'll cover some common use cases, summarising, inferring, transforming, expanding.

And then you'll build a chatbot using an LLM.

Thank you.

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 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 the magical forest of 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 LM 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.

Thank you.

In contrast, an instruction tuned LLM, which is where a lot of momentum of LLM research and practice has been going,

An instruction team 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 OOMs are typically trained is, you start off with a base OOM 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 TNLMs have been trained to be helpful, honest, and harmless.

So for example, they're less likely to output problematic texts, such as toxic outputs compared to base LLM.

a lot of the practical usage scenarios have been shifting toward instruction to,

you know, 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 to neural arms

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 DeepLearn .AI that had contributed to the materials that Ysia and I will be presenting.

I'm very grateful to Andrew Main, Joe Palermo, Boris Power, Ted Sanders, and Lillian Wang 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 Jeff Lodwick, Eddie Hsu 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 LM 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 holds the OM 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 you want snippets of texts

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 LOMs.

And you also learn from Ezer a second principle of prompting, that is giving LDLM time to think.

So with that, let's go on to the next video.

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

跟读练习: Prompt engineering - Introduction - 通过视频学习英语口语

关于本课

关键词汇与短语

练习技巧

什么是跟读法？