{"data":{"slug":"rag","etype":"concept","name":"Retrieval-Augmented Generation (RAG)","cat":"Training & Inference","subtitle":"Look it up, then answer — how enterprises ground models in their data.","summary":"RAG retrieves relevant documents (via embedding search) and feeds them into the model's context, giving current, citable, private knowledge without retraining. It is the default enterprise AI architecture and the reason vector databases and embedding models became infrastructure categories; long context windows compete with it, agents combine both.","attrs":null,"kf":null,"aliases":["retrieval augmented generation","vector database"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://arxiv.org/abs/2005.11401","label":"RAG paper"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T04:29:45+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}