Anthropic's Meta Prompt: A Must-try!

Summary

The video discusses the use of Anthropic's Claude models and the challenges of prompting AI differently from the OpenAI way. It highlights Anthropic's resources for effective prompting, including a prompt library, a GitHub cookbook, and a Metaprompt tool in a Google CoLab notebook. The Metaprompt system is praised for its ability to create detailed and effective prompts for specific tasks, offering a more refined approach than generic prompts and potentially improving the quality of AI-generated responses.

Takeaways

• 📚 The Anthropic Claude models require different prompting techniques compared to OpenAI models, highlighting the importance of adapting prompts to suit various AI systems.
• 🛠️ Anthropic has released a range of resources, including a prompt library and a cookbook on GitHub, to assist users in effectively interacting with their models.
• 📖 The concept of a 'Metaprompt' is introduced as a tool to interpret and structure prompts for large language models (LLMs), aiming to improve task execution and response quality.
• 🧠 The Metaprompt is designed to guide the AI in understanding and accomplishing tasks consistently, accurately, and correctly, emphasizing the need for careful instruction and examples.
• 🔍 Anthropic's Metaprompt is available as a Google CoLab notebook, allowing users with an API key to customize and generate core prompts for specific tasks.
• 📝 The Metaprompt includes detailed instructions and examples, encouraging users to think about task framing, exemplars, and input structure for better prompt engineering.
• 🎯 Prompts should be tailored to the AI's capabilities and the desired outcome, with longer and more detailed prompts often being more effective for complex tasks.
• 🔧 The use of exemplars in the Claude models is highlighted, with a structured format like HTML or XML being used to wrap task instructions and inputs.
• 📌 The importance of injecting company-specific tones and preferences into prompts is noted, allowing for the creation of more personalized and branded responses.
• 🚀 The Metaprompt can be a valuable tool for product development and for achieving a specific response style from large language models, enhancing the user experience.
• 💡 Users are encouraged to experiment with the Metaprompt and consider its application in building apps and agents, aiming for more precise and effective AI interactions.

Q & A

What is the main challenge when using different AI models for prompting?

The main challenge is that each AI model requires slightly different prompting techniques due to people being accustomed to the OpenAI way of prompting. This necessitates rewriting prompts to fit the specific model's requirements.

What kind of resources has Anthropic provided to assist with prompting their models?

Anthropic has provided a prompt library on their website, a cookbook on GitHub with various examples of how to use their models, and a Metaprompt tool in a Google CoLab notebook.

What is the purpose of the Metaprompt tool?

The Metaprompt tool is designed to help users create effective prompts for the Anthropic models by guiding them through the process of crafting a prompt that elicits a specific response or style from the language model.

How does the Metaprompt tool work?

The Metaprompt tool works by using a long, instructional Metaprompt that outlines how to write prompts for various tasks. Users fill out the notebook with their API key, select the model and task, input variables, and the tool generates a detailed prompt structure for the user to utilize.

Why is prompt engineering important for AI models?

Prompt engineering is crucial because it helps the AI model understand how to accomplish tasks consistently, accurately, and correctly. It provides the model with clear instructions and examples, which enhances its performance in completing the given tasks.

What is an exemplar in the context of the Claude models?

In the context of the Claude models, exemplars are examples of how to structure prompts for different tasks, presented in a format similar to HTML or XML, which include task instructions and inputs wrapped around the task.

How can the Metaprompt tool help in creating a better quality product?

The Metaprompt tool helps in creating a better quality product by providing a detailed and structured prompt that is more specific and tailored to the task at hand, resulting in more accurate and relevant outputs from the AI model.

What is the significance of the 'scratch pad' in the context of the Anthropic models?

The 'scratch pad' is a concept used in the Anthropic models for function calling and passing information back and forth. It allows for the manipulation of variables and data within the model's processing to achieve the desired output.

How does the Metaprompt tool address the issue of generic prompts?

The Metaprompt tool addresses the issue of generic prompts by encouraging users to provide detailed instructions and examples, which helps the AI model understand the specific requirements of the task and produce more targeted and effective responses.

What are some use cases for the Metaprompt tool?

Use cases for the Metaprompt tool include developing prompts for customer service emails, creating content for websites, and any scenario where a specific response or style is desired from the AI model.

How can users provide feedback or share their experiences with the Metaprompt tool?

Users can provide feedback or share their experiences with the Metaprompt tool by leaving comments on the video where the tool was discussed, or by reaching out to the Anthropic community for further discussion and support.

Outlines

00:00

🤖 Exploring Anthropic's Prompting Resources

The speaker discusses their experience with Anthropic Claude models and the resources available on Anthropic's website for crafting effective prompts. They highlight the challenge of adapting to different prompting styles for various AI models, emphasizing the need to rewrite prompts to suit each model's requirements. The speaker finds Anthropic's guides and tools particularly useful and notes the existence of similar resources from OpenAI. They introduce Anthropic's Metaprompt concept, which involves a system to interpret prompts from one language model to another, and mention the Google CoLab notebook provided by Anthropic as a tool for creating precise prompts.

05:02

📚 Importance of Detailed Prompts and Examples

The speaker delves into the common mistake of using overly brief prompts for complex tasks and emphasizes the value of detailed prompts with examples, as demonstrated in Anthropic's Metaprompt. They discuss the structure of the Metaprompt, which includes task instructions wrapped in a format similar to HTML or XML, and the use of exemplars to guide the AI model. The speaker also touches on the concept of function calling and the use of a scratch pad for passing information. The Metaprompt's instructional nature is highlighted, along with the speaker's suggestion to experiment with it for various applications.

10:04

🛠️ Applying Metaprompts in Practice

The speaker illustrates how to apply Metaprompts in practice using the Google CoLab notebook provided by Anthropic. They describe the process of setting up the notebook with an API key for security and demonstrate how to select a model and define a task. The speaker provides an example of drafting an email in response to a customer inquiry about a course and explains how to input variables for the task. They discuss the benefits of using Metaprompts for creating more detailed and effective prompts, resulting in higher quality outputs compared to generic prompts. The speaker encourages viewers to experiment with Metaprompts and apply them to their own projects, offering a comprehensive look at the potential of this prompting approach.

Keywords

💡Anthropic Claude models

The Anthropic Claude models refer to a series of AI language models developed by Anthropic, a company specializing in AI research. These models are designed to interact with users in a conversational manner, and the video discusses various strategies for effectively prompting these models to perform tasks. The models are noted for their unique characteristics compared to other AI models like those from OpenAI.

💡Prompting

Prompting is the process of providing input or a starting point for an AI language model to generate a response or perform a task. Effective prompting is crucial for guiding the AI to produce desired outputs. The video emphasizes the importance of understanding how different AI models require distinct prompting strategies to achieve optimal results.

💡UI (User Interface)

User Interface (UI) refers to the point of interaction between a user and a computer program, system, or device. In the context of the video, it refers to the graphical interface through which users can interact with the Anthropic Claude models, as opposed to the API (Application Programming Interface) which is a set of rules and protocols for building and interacting with software applications.

💡API (Application Programming Interface)

An Application Programming Interface (API) is a set of protocols and tools for building software applications that specify how different software components should interact with each other. In the video, the API is one of the ways users can interact with the Anthropic Claude models, programmatically, as opposed to using the UI.

💡Prompt Library

A Prompt Library is a collection of examples or templates designed to help users effectively prompt AI language models. It provides different ways of phrasing questions or tasks to achieve better responses from the AI. The video script highlights the existence of such a library on Anthropic's website as a resource for users.

💡GitHub

GitHub is a web-based hosting service for version control and collaboration that allows developers to store projects, track changes, and work together on code. In the context of the video, Anthropic has a GitHub repository that contains a 'cookbook' of examples and guides on how to use their AI models effectively.

💡Metaprompt

A Metaprompt is a system or method for creating prompts for AI language models that are designed to be interpreted by another AI or a more advanced prompting system. It involves crafting a prompt that is not only understandable to the AI but also structured in a way that it can generate a secondary prompt. The video discusses the concept of Metaprompts and how they can improve the quality of responses from AI models.

💡Google CoLab

Google Colaboratory, or CoLab, is a cloud-based platform for machine learning and data analysis that allows users to write and execute Python code in a collaborative environment. In the video, it is mentioned as the platform where Anthropic's Metaprompt notebook is hosted, enabling users to experiment with prompt engineering for their AI models.

💡Prompt Engineering

Prompt Engineering refers to the process of designing and refining prompts for AI language models to elicit the most accurate and useful responses. It involves understanding the nuances of how different AI models interpret and respond to various types of prompts and adjusting the prompts accordingly.

💡Function Calling

Function Calling is a programming concept where a function, which is a reusable piece of code, is invoked or 'called' to perform a specific task. In the context of the video, it refers to the process of invoking functions within the AI model to perform certain actions or generate outputs, such as handling variables and managing the flow of a conversation.

💡Multimodal

Multimodal refers to the ability of a system to handle or process multiple types of input or output data, such as text, images, audio, etc. In AI, multimodal capabilities allow models to understand and generate responses that incorporate different modes of communication, enhancing the interaction experience.

💡Scratch Pad

A Scratch Pad is a temporary storage area used in programming for holding data that needs to be accessed or modified during the execution of a program. In the context of the video, it refers to a feature within the AI model that allows for the passing of information back and forth between different parts of the conversation or different functions within the model.

Highlights

The speaker discusses their experience with Anthropic Claude models and the unique prompting techniques required for different AI models.

Anthropic has released a variety of resources on their website to assist with prompting their AI models effectively.

The speaker highlights the challenge of adapting to the Anthropic model's prompting style, which differs from the OpenAI approach.

The speaker mentions a prompt library available on Anthropic's website for reference on crafting prompts for their models.

Anthropic's GitHub repository contains a 'cookbook' with examples of how to use their models for various tasks, similar to OpenAI's approach.

The concept of a 'Metaprompt' is introduced, which is a system for interpreting prompts from one language model to another.

Anthropic has released a Google CoLab notebook that assists users in creating Metaprompts for their models.

The Metaprompt is instructional and provides guidance on prompt engineering for Anthropic's Claude 3 models.

The speaker notes that the Metaprompt is long and detailed, emphasizing the importance of thorough instructions for the AI.

The use of exemplars in the Claude models is discussed, which involves framing tasks within a structured format like HTML or XML.

The speaker points out that prompts often need to be longer and more detailed for complex tasks, contrary to common practice.

The speaker demonstrates how the Metaprompt tool can be used to draft an email responding to a customer inquiry about a course.

The tool allows users to input variables and provides a structured approach to crafting prompts with detailed instructions.

The speaker emphasizes the potential of Metaprompts in creating better prompts for AI models, leading to higher quality outputs.

The concept of Metaprompts is not new, with OpenAI's Dall-e system having used similar techniques for image creation.

The speaker suggests that Metaprompts could be useful for rewriting customer queries for better processing in AI systems.

The speaker encourages experimentation with Metaprompts to improve the quality of AI agent interactions.

The speaker concludes by highlighting the value of the Metaprompt tool and its potential applications in product development and team collaboration.

Transcripts

Okay.

So in the process of playing with the Anthropic Claude models, both on the

UI and also the API, I came across a number of interesting resources

that they've got on their website for basically helping you how to prompt this.

And this is something that I've been looking at recently for

a number of different models.

It's not just Anthropic specific.

but one of the challenges that a lot of the models have is that everybody is so

used to the OpenAI way of prompting that each of the models kind of needs to be

prompted in slightly a different way.

So this is one of the things that people would say that you couldn't

get the Gemini models to do certain things that the OpenAI models could do.

And the thing that I found is you could, but you needed to

rewrite the prompt in some way.

And that might be that you need to change the context, perhaps you need to change

the phrasing, et cetera of how you're actually getting the model to do this.

Now, of course the same thing is true for the Anthropic models, right?

That they have a different feel about them than the OpenAI models.

So I thought it was very interesting that Anthropic themselves has

basically released a bunch of guides and tools and stuff like

that around prompting their models.

So this is the first one that I found.

This is basically just like a prompt library where you can come in here,

you can look up different things.

The other day we were looking at sort of doing some things with websites,

so you can see How you would write the system prompt, how you would

basically customize a user prompt, et cetera, for this kind of thing.

And I think that there are lots of things out there, kind of like this.

The other ones that I found really interesting was their

whole cookbook on GitHub.

So on GitHub, they've got a whole cookbook of doing different things with

function calling doing different things with multimodal and stuff like that.

And, OpenAI has a really nice cookbook as well that you can go and look at

and see how they've done things there.

The third one and the one that I wanted to really focus on in this video

is this whole idea of a Metaprompt.

Now this is something I know that a number of the other language

model companies have looked at.

'Because I've heard people talk about it when giving feedback and stuff like that.

and this is the whole idea of having some kind of system that can interpret

a prompt from one LLM to another LLM.

Or to basically, have a Metaprompt that works out, okay, if you want that, you

need to write the prompt in a certain way.

So this is what Anthropic has released here.

They basically put it in a Google CoLab notebook.

and allows you to sort of go through and fill out the notebook as long as

you've got an API key, and then it will write the sort of core prompt

for you of what you should be doing.

So I think this is a really useful tool for When you want to make a product

or when you want a very specific kind of Response or style coming

back from the large language model.

So let's jump into the Google CoLab and have a look at actually how this

works and give it a little test run.

All right.

So this is the Metaprompt Colab here.

that's from Anthropic, I've modified it a little bit just to basically

put in, the Anthropic API key.

So using secrets in CoLab, you should definitely be doing that.

It just makes your notebooks a lot safer and also makes it a lot easier

to basically use these kinds of things.

so first you come through and it will install the Anthropic package for you.

and then basically you set it up with your key.

Now you can pick the model.

I'm going for the Opus model, but you could actually go for, the sonnet model,

I guess, if you wanted to do that.

And so basically they've got the, sort of idea here is that

they've got this Metaprompt.

Now the Metaprompt in itself one is very long, but two it's quite instructional

too, about prompt engineering and prompt engineering on the Anthropic

Claude 3 models, I would say.

so you can see that basically it sort of starts out with today you'll be writing

instructions to an eager, helpful, but inexperienced and unworldly AI

assistant who needs careful instruction.

And examples to understand best how to behave.

I will explain the task to you.

You will then write the instructions that will direct the assistant on

how best to accomplish the task consistently, accurately and correctly.

Here's some examples.

So the first thing it's doing is basically, setting the sort of frame.

it's interesting that they're going to be using exemplars in here.

And exemplars in the Claude models, they tend to sort of do it with a kind of

a HTML or XML sort of format of where basically, got task instruction, you're

wrapping the task, you've got inputs.

Now, these are things that can be injected in later on.

And you'll see that it basically goes through a set of exemplars

Of how to sort of do this for a variety of different tasks.

So it's just sort of priming the model to be able to do a

variety of different tasks.

Now, one of the things that I see people make the biggest mistake normally is

that their prompts, when they're trying to do something, reasonably complicated,

their prompts are just way too short for agents for things like this.

And I think this really kind of enforces that if you go through and

sort of look at how many different examples that they've got in here.

now, maybe you could argue that some of them are not, needed and stuff like that.

but I do find it's like, Very interesting to sort of see that this

is what they're sort of saying is best practice for their model for this.

It's also got some examples of doing function calling in here

as well, using the scratch pad, passing things back from the scratch

pad, et cetera going through this.

And then it finally ends off with these instructions.

So it talks about, to write your instructions, follow these instructions.

And then it gives it information about the input tags, the input

structure and those kinds of things.

And then at the end, it has a bunch of these notes, which I think are

kind of interesting in themselves.

So things like this is probably obvious to you already, but you

are not completing the task here.

You are writing the instructions for an AI to complete the task.

another name for what you're writing is a prompt template.

So it's kind of giving the model lots of things to sort of attach, to this.

Now, then you come basically down here.

And, you select out what it is that the tasks that you want it to do.

Now, I've put it in a really simple example of draft an

email, responding to a customer, inquiring about attending a course.

Okay.

the original example that they had in here was draft an email, responding

to a customer complaint in the app.

Now here's where it gets interesting.

So at this point you can pass in sort of what the variables should be.

So for example, if you're doing the customer complaint thing, you could pass

in the variables being one, a customer complaint, and two, the company name.

So you wouldn't actually pass in the customer complaint yet.

You're just telling it that, Hey, reserve a variable, for this.

Then we're going to use that with the actual prompt that it

generates for doing the task.

You can see another one, choose an item from a menu for me given my

preferences, then you've got the menu and then you've got preferences would

be the inputs that go with these.

Now if you leave it blank, like I do, you're actually leaving it so

that the model itself can decide what it thinks should be the input.

So I think that's kind of interesting in itself.

if I was really doing something for putting something in production, I would

certainly do one or a couple of runs where I just get it to decide the variables

and see what does it kind of think that it needs to be able to do this task.

All right.

once we've done that, we've got the task, we've set these variables to be either

empty or we put some in, we can actually just go through and sort of run the,

the first sort of prompt making parts.

So this is now where what it's going to do is it's going to

basically work out the Metaprompt.

what it's going to do is it's going to basically use the Metaprompt to

create back the prompt that you want.

So remember, my one was respond to an email about courses in here.

and so you can see here that it's basically done this.

It's decided, I guess, that, you need the customer email, which

the customer has sent in, and then the course details for this.

Now I could've probably made the task a lot more detailed and provided, that this

is in response to a form on a website.

the details that we have about them are their name, about the company, their

email, that kind of thing that's in there.

and then it basically does this instruction, structure.

here, it's going through and it's writing out, the specific things.

Now you can see that this is broken down into a lot more

detail than we would normally get.

if we were probably just writing this into a box ourselves.

and I think this is where the Metaprompt really can shine.

Is that when you've got these kind of things, it can really assist in getting

just a much better prompt for this.

This idea is not new.

OpenAI's Dall-e has used this for a long time.

both for good things and bad things, right?

So if you look at their sort of Dall-e system prompt, they have things

in there filtering out any artists that, might have copyrighted work.

they want to give diverse responses.

Obviously Google recently got into, a lot of hot water with their prompt,

rewriting that they were doing.

So when people were writing something in, there was then like another Metaprompt

that was writing it for the images.

We tend to see it more, for, I guess we've tended to see it more

for image creation than just sort of text response, stuff like that.

But this is definitely something that's useful and you could build

yourself if you had customers inputting something and you want to rewrite

their prompt, if someone says, show me my balance or something like that.

And you know that you've got certain information about what

their account is this and that you could rewrite the prompt, which

will then go to some other system.

Now I've done some examples of this, of rewriting queries for RAG.

is a very common thing where you basically do a rewrite of the query to

do a better search, that kind of thing.

okay, so we can see that we've got this sort of instruction structure.

We've got the instructions.

being, a much more specific.

And then we can see the inputs that at once here is the customer

email, which is wrapping in XML.

and the course details again, wrapping it in XML.

it's goes through, gives us some more details in here.

And then at the end, it's got this, remember be polite,

positive, professional tone.

And this is where you could actually sort of inject certain

things about your company.

the idea is that once you're not going to play and perhaps run this every time

you're going to then take this prompt.

and then reuse it many times or give it to people on your team or give it to people,

on your staff, that kind of thing here.

All right.

So we've got that.

and then finally, if we want to actually run it on what we've got in here.

we can actually go through and we can run it.

Where we pass this in and it will then prompt us.

it will then basically prompt us for the different things.

it prompted us here, for the customer email I typed in this.

it then prompted for, the course details.

I put, some course details in there, et cetera.

And then it went on to basically generate it's output for this.

And it wrote the email.

And I think this is definitely a better quality product than if it was just

something that was, just pasting in a small prompt, Into Claude or into ChatGPT

or into Gemini, any of these things.

so the idea of this Metaprompt and the way that they've given the CoLab for doing

this, I think is actually really cool.

And it's definitely worth you experimenting with this and sort of

seeing, okay, how could you apply this to the apps that you're building, to

the agents that you were building?

I think this is.

Giving some really nice prompting for agents where, when I see a lot

of the prompts that people write for agents, they're just very generic.

and then they're not really specific enough to the tool or to the use that the

person wants to actually get out of this.

Anyway, have a play with it.

let me know what you think in the comments.

As always, if you've got questions put in the comments below.

if you found the video useful, please click like and subscribe.

And I will talk to you in the next video.

Bye for now.