gpt-prompt-engineer

gpt-prompt-engineer
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gpt-prompt-engineer is a tool that takes the experimentation of prompt engineering to a new level. Users can input a task description and test cases, and the system will generate, test, and rank prompts through models like GPT-4, GPT-3.5-Turbo, Claude 3 Opus.

Key features include prompt generation, testing with an ELO rating system, classification handling, and support for Anthropic's Claude 3 Opus model for advanced learning. Various tools like Weights & Biases for logging and enhancements like the Claude 3 Opus->Haiku conversion version further extend capabilities. Set up is simple using a Google Colab or local Jupyter notebook environment.

Key Features

Prompt Generation
AI
ELO Rating System
Classification
Testing

Pros

  • Automates prompt generation and testing processes.
  • Utilizes multiple AI models including GPT-4 and Claude 3 Opus.
  • ELO rating system provides clear prompt performance ranking.
  • Support for advanced models like Claude 3 enhances flexibility.
  • Simple integration via Google Colab or Jupyter notebook.

Cons

  • Potentially expensive with many prompts generated.
  • Requires API keys for full functionality.
  • Advanced features may have a steep learning curve for beginners.
  • Reliant on external AI models which may limit control.
  • Limited support for non-classification use cases.

Frequently Asked Questions

What is the primary function of the gpt-prompt-engineer tool?

It generates, tests, and ranks AI-generated prompts to find the most effective ones for a given use-case.

Which AI models does the tool support?

The tool supports GPT-4, GPT-3.5-Turbo, Claude 3 Opus, among others.

How does the ELO rating system work in gpt-prompt-engineer?

Each generated prompt is assigned an ELO rating based on their test performance, allowing the most effective prompts to rank higher.

What are the Claude 3 Opus -> Haiku conversion features?

It allows for the creation of performance-driven AI systems by leveraging Claude 3 Opus for latent space and Claude 3 Haiku for output generation.

How can users set up and use gpt-prompt-engineer?

Users can set it up on Google Colab or a local Jupyter notebook, adding API keys for model access, then input descriptions and test cases to start generating prompts.

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