RAG (Retrieval-Augmented Generation) — Retrieval-Augmented Generation
What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is the technique of searching a business's own documents, knowledge base, or database for relevant passages at the moment a question is asked, then feeding those passages into a language model's prompt so its answer is grounded in that specific content instead of relying only on what the model memorized during training. RAG is the standard way to make an AI agent answer accurately about a company's own products, policies, or account data.
More detail
A RAG pipeline typically embeds documents into a vector index ahead of time, then at query time retrieves the passages most semantically similar to the incoming question before generating a response.
RAG reduces hallucination risk by grounding answers in retrieved source text, but the quality of retrieval — how well the right passages are found — matters as much as the model generating the final answer.
Frequently asked
- Why use RAG instead of just fine-tuning a model on company data?
- RAG lets an AI agent answer from current, easily updated source documents without retraining the underlying model every time content changes, which is faster and cheaper than fine-tuning for information that updates frequently, like pricing or policy documents.
- Does RAG eliminate hallucination entirely?
- No — it significantly reduces it by grounding answers in retrieved source text, but if retrieval surfaces the wrong or incomplete passages, the model can still generate an inaccurate answer, so retrieval quality is a critical factor.
See also
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