Retrieval augmented generation (RAG) pairs an information search step with an LLM. Before generating text, the system fetches relevant documents or facts and inserts them into the prompt. This reduces hallucination and keeps responses aligned with current data.
Implementations range from simple vector database lookups to custom toolchains that search internal knowledge bases. The key is selecting high quality sources and formatting them for concise context.