Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Source
arxiv.org
Author
Patrick Lewis et al.
Date
Why it matters
The original formulation of what the industry now just calls RAGRetrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.Full definition →: pair a generator with a dense retrieval index so knowledge lives in a store you can update rather than in weights you must retrain. Worth reading to see how much of what people improvise was specified here.
Terms in this piece · Glossary
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
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