RAG

Retrieval-Augmented Generation (RAG) pulls relevant context from an external corpus and stuffs it into a model’s prompt before generation, letting the model answer based on freshly retrieved facts rather than just its weights. It’s the dominant pattern for grounding LLMs in private or up-to-date data.