This repository offers a curated collection of experimental prompts, tools, and scripts for interacting with a wide range of large language models (LLMs) from major providers. It targets developers and researchers seeking to explore and optimize prompt engineering techniques across various domains like writing, programming, and finance. The primary benefit is a battle-tested library of prompts and utilities to enhance LLM performance and efficiency.

How It Works

The library organizes prompts into categories, with an INDEX.md file serving as a client-side rendered catalogue. This approach allows for quick browsing and direct access to prompt examples without requiring server-side processing for the catalogue itself. The project also includes several Python-based helper scripts for prompt validation, mixing, token counting, analysis, and evolution, facilitating a more systematic approach to prompt engineering.

Quick Start & Requirements

• Install helper scripts: pip install -r scripts/requirements.txt
• Rebuild catalogue: python scripts/build_index.py
• Official Demo: https://abilzerian.github.io/LLM-Prompt-Library/

Highlighted Details

• Over 50 prompts across categories including Writing & Editing, Programming, Prompt Generation, Medical, Finance, and Miscellaneous.
• Includes tools for prompt validation, mixing, token counting, analysis, and evolution.
• Features specialized prompts like "UnstructuredText → JSON" and "10-K Analyzer".
• Supports LLMs from Apple, DeepSeek, OpenAI, Anthropic, Meta, Mistral, Google, and xAI.

Maintenance & Community

Maintained by Alex Bilzerian.

Licensing & Compatibility

Distributed under the MIT License. This license is permissive and generally compatible with commercial and closed-source applications.

Limitations & Caveats

The library is described as containing "highly experimental" prompts, suggesting potential instability or unproven effectiveness for certain use cases. The "Prompt Evolution" tool, if implemented, could involve complex iterative processes.