Retrieval-Augmented Generation (RAG) allows an LLM to answer questions using your data at query time. On their own, LLMs are powerful but limited: they can hallucinate, they have a fixed knowledge cutoff, and they know nothing about your private documents, internal wikis, or proprietary systems. RAG bridges that gap by retrieving relevant information from your data and supplying it to the model as