PrecisionRAG

Prompt Tuning For Building Enterprise Grade - RAG Systems

Automatic Prompt Engineering

This project aims to develop an enterprise-grade Retrieval-Augmented Generation (RAG) system by automating the prompt engineering process. The goal is to create a comprehensive solution that simplifies the task of crafting effective prompts for Language Models (LLMs), enabling businesses to leverage advanced AI capabilities more efficiently.

Key Features

1. Automatic Prompt Generation: Automatically generate a diverse set of prompt options based on user input and objectives, saving time and effort in manual prompt engineering.
2. Automatic Evaluation Data Generation: Automatically create test cases and scenarios to comprehensively evaluate the performance of prompt candidates, ensuring prompt effectiveness in various contexts.
3. Prompt Testing and Ranking: Implement robust prompt testing and ranking mechanisms, such as Monte Carlo matchmaking and ELO rating systems, to objectively evaluate and prioritize the most effective prompts.
4. User-Friendly Interface: Develop a intuitive user interface to streamline the prompt engineering process, allowing users to input requirements, view generated prompts, and analyze evaluation results.

Getting Started

1. Clone the repository: git clone https://github.com/GetachewAbebeA/PrecisionRAG.git

markdown Copy 2. Install the required dependencies: cd automatic-prompt-engineering pip install -r requirements.txt

arduino Copy 3. Set up the development environment and run the application: python src/ui/app.py

markdown Copy

Contributing

We welcome contributions to this project. Please follow the standard GitHub workflow:

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Commit your changes
4. Push to your fork
5. Submit a pull request

License

This project is licensed under the MIT License.