An architecture where an LLM, before generating its response, first retrieves relevant documents from an external knowledge base and includes them in the prompt. Instead of relying solely on what the model learned during training, RAG lets the model answer using up-to-date, customer-specific, or proprietary information. In meeting copilots, RAG is what makes "what did we decide about the API redesign last quarter?" actually work: the system embeds the query, finds the most relevant transcript chunks via vector search, and passes them to the LLM with an instruction to answer only from the retrieved context. RAG is also the primary defense against hallucination in long-tail factual questions â if the right transcript isn't retrieved, a well-designed RAG system should say so rather than guess.