Knowledge
Retrieval-augmented generation, commonly called RAG, is a technique in which a language model's response to a query is grounded by first retrieving relevant documents from a defined knowledge base and providing those passages as context alongside the query. The model generates its answer from the retrieved material rather than solely from its training data, which reduces fabrication and allows the system to answer questions about proprietary or recent information.
A language model without retrieval generates answers from patterns learned during training. This works for general knowledge but fails for questions that require specific, current, or proprietary information, because the model will either lack the information or fabricate a plausible-sounding but incorrect answer.
RAG addresses this by separating two functions: search and generation. The search step finds the most relevant documents in a defined corpus; the generation step produces a response grounded in those documents. The quality of the system depends heavily on both steps, particularly the search component, which determines whether the right source material is retrieved.
In the context of institutional knowledge retrieval, RAG is the technical backbone that allows a query about how a firm approached a past engagement to surface the actual memo and answer from its contents, not from a fabricated recollection.
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