RAG
Expanded definition
Retrieval-augmented generation (RAG) combines a retriever (vector search, keyword/BM25, or hybrid) with a generator (usually an LLM). Production systems add chunking, metadata filters, re-ranking, citations, access control at query time, and eval loops because retrieval quality—not model size—usually dominates answer quality.
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RAG FAQ
What is RAG?
RAG retrieves relevant passages (often via embeddings) and conditions generation on them—reducing reliance on parametric memory alone.
How is RAG used in AI systems?
Retrieval-augmented generation (RAG) combines a retriever (vector search, keyword/BM25, or hybrid) with a generator (usually an LLM). Production systems add chunking, metadata filters, re-ranking, citations, access control at query time, and eval loops because retrieval quality—not model size—usually dominates answer quality.
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