AI
RAG (Retrieval-Augmented Generation)
Grounding an AI's answers in retrieved, relevant source data.
Retrieval-augmented generation combines an LLM with a retrieval step that fetches relevant documents or data, so the model answers using accurate, up-to-date, source-grounded information rather than memory alone.
RAG reduces hallucination and lets AI products cite sources — a pattern that also supports the trust and transparency UI users increasingly expect.
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