Anthropic's Meta Prompt: A Must-try!

Sam Witteveen
15 Mar 202412:34

TLDRThe video discusses the process of effectively prompting AI models, specifically those from Anthropic, which differ from the OpenAI models in their approach. The speaker highlights the importance of adapting prompts to suit the nuances of various AI models. Anthropic has provided resources, including a prompt library and a GitHub cookbook, to assist users in crafting effective prompts. The focal point is the Metaprompt, a tool that translates prompts from one language model to another, which is accessible through a Google CoLab notebook. The Metaprompt is detailed, instructional, and aids in creating more precise and effective prompts for specific tasks. The video demonstrates how to use the Metaprompt to generate a detailed and customized prompt for drafting an email response to a customer inquiry, emphasizing the benefits of a well-crafted prompt for producing higher quality AI outputs.


  • 🤖 The Anthropic Claude models require different prompting techniques compared to OpenAI models.
  • 📚 Anthropic provides a prompt library and guides on their website to assist users in crafting effective prompts.
  • 💡 The concept of a Metaprompt is introduced, which is a system that interprets a prompt from one LLM to another.
  • 📓 Anthropic has a GitHub cookbook with examples of how to use their models for various tasks.
  • 🔍 The Metaprompt is a tool that helps create a core prompt for specific tasks, enhancing the quality of responses from language models.
  • 📝 The Metaprompt Colab notebook from Anthropic allows users to input their API key and generate prompts tailored to their needs.
  • 📌 Prompts need to be detailed and instructional for AI models to understand and accomplish tasks accurately.
  • 📈 The use of exemplars within the Metaprompt helps the model understand the structure and context required for different tasks.
  • 📋 The Metaprompt process includes setting variables and providing detailed instructions for the AI to follow.
  • 📝 The final prompt generated by the Metaprompt is more detailed and specific than what a user might write manually.
  • 🚀 The Metaprompt tool can be used to improve the quality of prompts for AI applications and agents, leading to better responses.

Q & A

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

    -The main challenge is that each model, such as OpenAI or Anthropic, requires a slightly different way of prompting due to their unique characteristics, which can lead to confusion and misinterpretation of tasks.

  • What does Anthropic provide to help users prompt their models effectively?

    -Anthropic provides a set of guides, tools, and a prompt library on their website to assist users in crafting effective prompts for their models.

  • What is the purpose of the 'Metaprompt' concept introduced by Anthropic?

    -The Metaprompt is a system designed to interpret and rewrite prompts from one language model to another, ensuring that the prompt is structured in a way that is most effective for the target model.

  • How does the Metaprompt tool work in Google CoLab?

    -The Metaprompt tool in Google CoLab allows users to input their API key, select a model, and then follow a structured process to create a detailed and specific prompt for the model to execute a task.

  • What are the benefits of using the Metaprompt tool for developing products or applications?

    -The Metaprompt tool helps in generating high-quality, specific prompts that can lead to more accurate and consistent responses from language models, which is particularly useful for creating products or applications that require precise outputs.

  • How does the Metaprompt tool handle variables in the context of a task?

    -The tool allows users to specify variables that are needed for the task, such as customer complaints or company names, and reserves these variables to be filled in later with actual data when the prompt is used.

  • What is the significance of using exemplars in the Metaprompt?

    -Exemplars provide concrete examples of how to structure prompts for various tasks, which helps guide the language model and improve the quality of its responses.

  • How does the Metaprompt tool ensure that the final prompt is detailed and specific?

    -The tool breaks down the task into a structured format, providing detailed instructions and examples, which helps in creating a more precise and effective prompt for the language model.

  • What are some common mistakes people make when crafting prompts for language models?

    -A common mistake is using prompts that are too short or too generic for complex tasks, which can lead to incomplete or inaccurate responses from the language model.

  • How does the Metaprompt tool help in maintaining a consistent tone or style in the responses?

    -The tool includes instructions for maintaining a polite, positive, and professional tone in the responses, allowing users to inject specific company or personal styles into the prompts.

  • Can the Metaprompt tool be used for tasks other than text response, such as image creation?

    -While the tool is primarily discussed in the context of text response, the concept of Metaprompting can be applied to other areas like image creation, as seen in systems like OpenAI's Dall-e.

  • What is the potential use of the Metaprompt tool for businesses looking to automate customer service tasks?

    -The Metaprompt tool can be used to create detailed and specific prompts for automated customer service tasks, such as drafting emails or responding to inquiries, leading to more personalized and professional interactions.



🤖 Understanding Anthropic Claude Models and Prompting Techniques

The speaker discusses their experience with Anthropic Claude models, emphasizing the need for different prompting techniques compared to OpenAI models. They highlight the variety of resources available on Anthropic's website to assist with prompting, including a prompt library and a GitHub cookbook. The speaker also introduces the concept of a Metaprompt, a tool for translating prompts between different language models, which is particularly useful for creating specific responses or styles from large language models. The Metaprompt is demonstrated through a Google CoLab notebook that guides users in crafting detailed instructions for the AI.


📚 Prompt Engineering and the Metaprompt's Role in Task Performance

The paragraph delves into the importance of detailed prompts for complex tasks, contrasting the common mistake of using overly brief prompts. It outlines the structure of the Metaprompt, which includes exemplars and detailed instructions to guide the AI in performing tasks accurately. The speaker also discusses function calling and the use of a scratch pad within the prompt. The Metaprompt's detailed approach is shown to be beneficial for creating high-quality prompts, which can then be reused or shared among team members for consistency in task execution.


💌 Crafting Effective Email Responses with the Metaprompt

The speaker provides a practical example of using the Metaprompt to draft an email responding to a customer inquiry about attending a course. They explain how to input variables into the Metaprompt, such as customer complaints or company names, and how these can be used in the actual prompt generation. The process results in a detailed and structured prompt that guides the AI in creating a more refined and professional email response. The speaker also touches on the broader applications of the Metaprompt concept in various AI systems, including its use in image creation and query rewriting for improved search results.



💡Anthropic Claude models

Anthropic Claude models refer to a series of AI models developed by Anthropic, a company specializing in AI research and development. These models are designed to interpret and respond to prompts in a nuanced way. In the video, the speaker discusses their experience with these models, highlighting how they differ from OpenAI models and the unique prompting strategies required for effective interaction.


Prompting is the process of providing input or a query to an AI model to elicit a response. The video emphasizes the importance of crafting prompts that are tailored to the specific AI model being used, as different models may require different types of prompts to achieve the desired outcome. The speaker discusses the challenges and strategies associated with prompting various AI models.


A Metaprompt is a higher-level prompt designed to guide the construction of other prompts. It serves as a framework or template that can be used to generate more specific prompts for AI models. In the context of the video, the speaker explores a Metaprompt tool released by Anthropic, which helps users create effective prompts for their AI models by filling out a structured Google CoLab notebook.

💡Google CoLab notebook

Google CoLab, short for Google Colaboratory, is a cloud-based platform that allows users to write and execute Python code in their web browsers. In the video, the speaker mentions using a Google CoLab notebook provided by Anthropic to experiment with the Metaprompt tool. This notebook is filled out by the user, and with the help of an API key, it assists in generating the core prompt for a specific task.

💡API key

An API key is a unique identifier used to authenticate a user, developer, or calling program to an API (Application Programming Interface). In the context of the video, the speaker discusses the use of an API key to enable interaction with Anthropic's models through the Google CoLab notebook, allowing users to generate prompts and receive responses from the AI.

💡Opus model

The Opus model is one of the AI models offered by Anthropic. It is selected by the speaker in the video as the model they wish to use for their demonstration. The choice of model can affect how the AI interprets and responds to the prompts, and the Opus model is highlighted as an option within the Metaprompt tool for generating prompts.

💡Prompt engineering

Prompt engineering is the art and science of creating prompts that effectively guide AI models to produce desired responses. The video script delves into the intricacies of prompt engineering, particularly for the Anthropic Claude 3 models. It is portrayed as a critical skill for getting the most out of AI models, as demonstrated by the structured approach of the Metaprompt.

💡Function calling

Function calling refers to the process of invoking a function or a specific piece of code within a program to perform a task. In the video, the speaker briefly touches on the concept of function calling within the context of AI models, suggesting that it can be used to perform specific tasks or manipulate data within the scope of the AI's capabilities.


Multimodal refers to systems or interactions that involve multiple modes or forms of input, such as text, images, and voice. The video mentions a cookbook on GitHub with examples of how to work with multimodal inputs in AI models. This suggests that Anthropic's models can process and generate responses that incorporate different types of data.

💡Scratch pad

The scratch pad is a concept mentioned in the video where temporary data or intermediate results can be stored and passed back and forth within the AI model's operations. It is used as an example of how complex tasks can be broken down and managed within the AI's workflow, contributing to the overall efficiency and accuracy of the model's responses.


Exemplars are specific examples or instances that serve as a model or pattern to be followed. In the context of the video, exemplars are used within the Metaprompt to demonstrate how to structure prompts for various tasks. They act as a guide for the AI, helping it understand the expected format and content of the prompts it should generate.


Anthropic has released a guide on effective prompting for their AI models, including a prompt library and a Metaprompt tool.

The guide is not only useful for Anthropic-specific models but can be applied to various AI models that require different prompting techniques.

Anthropic's Metaprompt is a system that helps interpret a prompt from one language model to another, enhancing the quality of responses.

The Metaprompt tool is available as a Google CoLab notebook, which can be accessed with an API key for an interactive experience.

The notebook provides detailed instructions on prompt engineering, particularly for the Anthropic Claude 3 models.

Exemplars, or examples, are used in the Metaprompt to guide the AI in understanding how to perform tasks accurately.

The Metaprompt emphasizes the importance of detailed and long prompts for complex tasks, contrary to common mistakes made with shorter prompts.

The tool includes examples of function calling and using a scratch pad to pass information between tasks.

Instructions are provided on how to structure inputs and the importance of maintaining a polite, positive, and professional tone.

The Metaprompt can be used to rewrite user prompts for better task execution, which is particularly useful in customer service applications.

The concept of Metaprompting is not new, with OpenAI's Dall-e using it for image creation and filtering.

The notebook allows users to customize the Metaprompt for specific tasks, such as drafting emails or menu selections.

The output from the Metaprompt is of higher quality compared to using generic prompts with AI models like ChatGPT or Gemini.

The Metaprompt tool is recommended for developers building applications or agents that require specific responses or styles from AI models.

Anthropic's resources, including the prompt library and Metaprompt, are available on their website for users to explore and experiment with.

The video encourages viewers to try out the Metaprompt and share their experiences, suggesting a collaborative approach to improving AI interactions.