What is Retrieval-Augmented Generation (RAG)?
RAG is a technique that grounds a language model's answers in your own data by retrieving relevant documents at query time and feeding them to the model as context. It reduces hallucination and lets an LLM answer from private, up-to-date knowledge.
A RAG pipeline embeds your content into a vector database, retrieves the most relevant chunks for a given question, and passes them to the LLM alongside the prompt. It's the standard pattern for building trustworthy, source-cited enterprise assistants without retraining a model.