RAG (Retrieval-Augmented Generation)

Retrieval-augmented generation (RAG) is an architecture pattern that connects a large language model to external knowledge sources—documents, databases, APIs—so its responses draw on real, current information rather than relying solely on what it memorized during training. The model retrieves relevant context at query time, then generates an answer grounded in that evidence. RAG is one way enterprises make general-purpose AI useful for their specific business, though the approach is not without its limitations and has been surpassed by simpler read/write/grep tools in many instances.