Retrieval-augmented generation (RAG) is a way of making an AI answer from your information rather than from memory. Before the model replies, the system searches your documents, price lists or help pages for the passages that match the question, and passes them to the model along with the question. AWS describes it as making a model reference "an authoritative knowledge base outside of its training data sources before generating a response".
Why it matters for your business
A large language model on its own knows nothing about your business: not your prices, your return policy or the doctor who works on Tuesdays. Ask it anyway and it may guess. RAG is the usual fix. It is how most business chatbots and assistants answer questions about you without retraining a model.
It has three practical benefits. Answers can stay current, because you update a document instead of a model. Answers can point to their source, which AWS notes lets the model present information "with source attribution". And you control what the AI is allowed to draw on.
How RAG works, in four steps
- Prepare. Your documents are split into short passages and indexed so they can be searched by meaning, not only by exact words.
- Retrieve. When a question comes in, the system finds the few passages most likely to contain the answer.
- Augment. Those passages are added to the prompt, with an instruction such as "answer only from these".
- Generate. The model writes the reply, ideally saying which passage it used.
An example
A training institute has a 40-page course handbook. A student asks on WhatsApp, "If I miss the March batch, can I join in June without paying again?" The system retrieves the paragraph on batch transfers and the one on fees, and the model answers from those two passages. If the handbook says nothing about it, a well-built system says so and passes the question to staff instead of inventing a policy.
What to do about it
RAG is only as good as the documents behind it. Before any build, gather the real answers in one place, remove old versions, and fix contradictions (two price lists is a common one). Then test with real customer questions, including ones the documents cannot answer, and check that the system admits it does not know. Plain RAG reduces wrong answers; it does not remove them, so keep a person in the loop for anything that commits you to money or policy.
The What is an LLM? guide covers grounding in more detail. We build RAG into assistants and internal tools as part of our custom AI agents service.
Frequently asked questions
Is RAG the same as training an AI on my data?
No. Training changes the model itself, which is slow and costly. RAG leaves the model unchanged and shows it the relevant parts of your documents each time it answers, so updating an answer is as simple as updating the document.
What documents can RAG use?
Most text you can export: PDFs, web pages, help articles, spreadsheets, policies and past email answers. Scanned images and messy tables need cleaning first. The cleaner and more current the documents, the better the answers.
Does RAG stop AI hallucinations?
It reduces them a lot, but not to zero. The model can still misread a passage or answer when retrieval found the wrong one. Instructing it to quote its source and to say "I don't know" when nothing matches helps further.
Last reviewed 22 Oct 2026 by the Astrafolio team.