RAG combines search with generation: before answering, the system retrieves relevant documents (usually via embeddings), places them into the prompt, and the model answers grounded in that material instead of memory alone.
RAG is the standard answer to two LLM weaknesses: outdated knowledge and hallucination. Answers anchored in retrieved, citable documents are easier to trust and verify.
Ask a company chatbot about this year's pricing: RAG fetches the current price sheet into the prompt, so the answer reflects today's document, not last year's training data.