RAG Tools

RAG stands for Retrieval-augmented generation, a technique that enhances LLM responses by retrieving relevant information from a specified knowledge base before generating an answer.

RAG tools answer user questions by embedding them into a vector store, semantically searching for related vectorized data and generating a response grounded in retrieved data. Most common use case for RAG Tools is a Q&A system.

RAG Tools cannot exist without Knowledge Sources (otherwise the LLMs would make up their own answers to user questions) and Prompt Templates, that determine the format, structure, and versatilty of the LLM responses. Also, Prompt Templates optionally ensure on-the-fly content translation and post-processing of RAG queries.