Retrieval-augmented generation gives a model knowledge it wasn’t trained on by fetching relevant text at query time and stuffing it into the context window. The flow: embed the user’s question, search a vector database for the nearest embeddings, and paste the top matches into the prompt as grounding.
RAG is the standard way to build assistants over private or fast-changing data (docs, tickets, wikis) without retraining. Done well, it also reduces hallucination by giving the model something real to quote. Done poorly, it retrieves the wrong chunks and confidently misleads.