RAG
Retrieval-augmented generation combines a retriever — vector search over an indexed corpus — with a large language model so answers are grounded in cited source documents instead of the model's training data. RAG cuts hallucinations and lets answers stay current as your corpus updates.
RAG pipeline,retrieval augmented generation,retrieval-augmented generation
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