MindBlowing META PROMPT To Produce Better Output

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AI Nuggets

Detailed Instructions, CLI Commands, Website URLs, and Tips from Video Transcript

Meta Prompt Template

• Description: A meta prompt template to enhance language models with task-agnostic scaffolding.
• Prompt Example:

  You are a meta expert, an extremely clever expert with the unique ability to collaborate with multiple experts such as expert Problem Solver, expert Mathematician, expert Essayist, etc. to tackle any task and solve any complex problems. Some experts are adept at generating solutions while others excel at verifying answers and providing valuable feedback. Note that you also have special access to expert Python, which has the unique ability to generate and execute Python code given natural language instructions. Expert Python is highly capable of crafting code to perform complex calculations when given clear and precise directions. As a meta expert, your role is to oversee the communication between the experts effectively using their skills to answer a given question while applying your own critical thinking and verification abilities.  
  
  To communicate with an expert, type its name (e.g., "expert Linguist" or "expert Puzzle Solver") followed by a colon, and then provide detailed instructions enclosed with triple quotes. For example:  
    
  expert Mathematician:   
  

  "Compute the Euclidean distance between the points (-2, 4) and (3, -1)."  
  

  • Ensure instructions are clear and unambiguous.
  • Interact with only one expert at a time.
  • Break complex problems into smaller solvable tasks.
  • Include all relevant details in every call.
  • If an expert makes a mistake, ask a new expert to review and give feedback.
  • Always provide complete information in instructions.
  • Seek multiple opinions or independently verify solutions if uncertain.
  • Consult an expert for confirmation and aim to present the final answer within 15 rounds or fewer.
  • For multiple-choice questions, select only one option and analyze the provided information carefully.

Research Paper

• URL: The link to the research paper is provided in the show notes of the video.

Autogen Team’s Auto Build Feature

• Description: Autogen’s Auto Build feature generates all relevant agents from scratch based on the given problem.
• Tip: Use the Auto Build feature to dynamically create agents for specific tasks.

Prompting Methods

• Standard Prompting: Directly asking an LLM to yield a response without any specific guiding input.
• Zero-shot Chain of Thought Prompting: Appending “Let’s think step by step” to the input query.
• Expert Prompting: Crafting an expert identity tailored to the specific context of the input query.
  • Static Expert Prompting: Uses a fixed and generic expert description.
  • Dynamic Expert Prompting: Designs a new expert identity for each input query.
• Multi Persona Prompting: Also known as Solo Performance Prompting.
  • Propose a small ensemble of personas.
  • Let these personas engage in a collective dialogue.
  • Synthesize all available information to deliver a final response.

Performance Comparison

• Task List: 8 different tasks were tested.
• Results: The meta prompt with Python abilities showed a significant improvement in performance compared to basic prompting methods.

Limitations of Meta Prompting

• Token Usage: Requires more tokens due to the use of multiple experts and frequent LLM queries.
• Other Limitations: Not specified in the provided transcript.

Adam Smith’s Specialization Concept

• Relevance: The concept of specialization and division of labor by Adam Smith is related to the idea of using multiple specialized agents in automation and LLMs.
• Benefits of Specialization:
  • Increased productivity and skill improvement.
  • Time-saving due to reduced task switching.
  • Innovation and invention of new tools.
  • Process improvement and economic efficiency.

Video Summary

• The video discusses the benefits of using a meta prompt template for enhancing language models and compares different prompting methods. It also touches on the concept of specialization in economic progress and its relevance to using specialized agents in LLMs.

Call to Action

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(Note: The detailed URLs for the research paper and other resources are to be found in the show notes of the video, which were not provided in the transcript.)