Retrieval-Augmented Generation (RAG)

Also known as RAG

Retrieval-Augmented Generation (RAG) is an architecture that retrieves relevant documents or passages at query time and feeds them into an LLM's context so it answers from your data instead of its training memory. It trades a training-time knowledge problem for a retrieval-quality problem — chunking, embeddings, and ranking now decide whether the answer is grounded or a plausible-sounding guess. Most "AI doesn't know our data" complaints are RAG pipeline defects, not model limitations.

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