RAG（检索增强生成）

A technique that lets LLMs retrieve external documents before generating an answer.

RAG finds the most relevant passages via vector search and feeds them to the model alongside the question, reducing hallucination and adding up-to-date knowledge. Compared with fine-tuning, RAG needs no training and is cheap to update.