Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases

Retrieval-Augmented Generation (RAG) solves the problem of Large Language Models providing incorrect or outdated answers for enterprise-specific questions. The system works by retrieving relevant documents from an internal knowledge base at query time and providing them as context to the LLM. This guide details a two-part architecture consisting of an indexing pipeline and a retrieval/generation pipeline. The indexing process involves loading documents, chunking them effectively, creating vector embeddings, and storing them in a vector database like Weaviate that supports hybrid search. The retrieval pipeline then uses this index to find the most relevant information to ground the LLM's response, ensuring answers are accurate and traceable to a source.