Open Source RAG Prompt Library

An open-source prompt library dedicated to the implementation of Retrieval-Augmented Generation (RAG) systems with large language models (LLM) has been released. The initiative aims to provide useful resources for developers and researchers working with this architecture.

The library includes prompts designed to address several typical challenges of RAG systems, including:

  • Grounding constraints: Ensuring that the model's responses are based on relevant and verified information.
  • Citation rules: Correctly managing citations of the sources used to generate the responses.
  • Handling uncertainty and multiple sources: Integrating information from different sources, managing any conflicts or uncertainties.

The prompts are provided in an easily usable format (copy-and-paste), and the community is invited to contribute new prompts and evaluate existing ones through a voting system. The goal is to continuously improve the quality and effectiveness of the available prompts.

Background on RAG systems

RAG systems combine the ability of language models to generate text with the ability to retrieve information from external sources. This approach makes it possible to create systems capable of providing more accurate, relevant, and up-to-date data-based responses. Prompts play a crucial role in determining the behavior of the model and the quality of the generated responses.