RAG fetches relevant chunks from a knowledge base at query time and stuffs them into the model's context. Answers cite the retrieved passages, which reduces hallucination and lets teams update knowledge without retraining.
Weaknesses: retrieval quality caps answer quality, retrieved content can carry indirect prompt injection, and long contexts get expensive fast.