This repository provides a curated collection of prompts designed for legal prompt engineering with large language models. It aims to enhance efficiency and knowledge sharing in the legal tech space, targeting legal professionals and AI researchers interested in applying LLMs to legal tasks. The project offers structured approaches for legal analysis and document processing, leveraging established legal reasoning methodologies.

How It Works

The project utilizes several prompt engineering methodologies, including IRAC (Issue, Rule, Application, Conclusion) and its variations (TRRAC, CLEO, ILAC, etc.), as validated by academic research. It also incorporates STAR (Situation, Task, Action, Result) and Chain of Thought prompting. These structured approaches guide LLMs to perform tasks like legal translation, clause summarization, agreement drafting, and legal research, aiming for improved accuracy and professional output.

Quick Start & Requirements

• Installation: No explicit installation instructions are provided; prompts are designed for direct use with LLM interfaces.
• Prerequisites: Access to a capable LLM (e.g., GPT-3.5, GPT-4) is required.
• Resources: Usage is dependent on the LLM provider's infrastructure.
• Links: The README contains examples and explanations of various prompt structures.

Highlighted Details

• Methodology Benchmarks: Provides accuracy metrics for different IRAC-like legal reasoning approaches.
• Diverse Legal Tasks: Covers translation, summarization, contract drafting, citation generation, and legal research.
• Multilingual Support: Includes prompts for both English and Chinese legal contexts.
• Contributor-Driven: Encourages community contributions for expanding the prompt library.

Maintenance & Community

The project is actively maintained by contributors, with @WuyueTracyWang being a prominent contributor. Community engagement is encouraged via direct contributions to the repository.

Licensing & Compatibility

The repository does not explicitly state a license. Users should assume all rights are reserved or contact the maintainer for clarification.

Limitations & Caveats

The effectiveness of the prompts is highly dependent on the underlying LLM's capabilities and any specific fine-tuning. The project is a collection of prompts rather than a standalone tool, requiring integration with an LLM platform. Accuracy metrics are based on specific research and may vary in different applications.