How RAG works
A RAG system starts by indexing your material — your wikis, contracts, support tickets, whatever holds the answer — using embeddings, a way of representing each passage so it can be found by meaning rather than by matching the exact words someone typed. When a question comes in, the system searches that index (the step people call vector search) and pulls back the handful of passages that actually address it. Only then does a language model write a response, composed from those retrieved passages rather than from memory. Index, retrieve, generate — three steps, in that order, which is where the name comes from.
Why it matters for your business
This is what makes an AI answer verifiable. A general-purpose model has no idea what your return policy says, what a customer paid last quarter, or what your current price sheet looks like — it was never trained on any of that, so left to itself it guesses, and it sounds exactly as confident when the guess is wrong as when it is right. RAG is what closes that gap: the system can answer about your business at all because it is reading your business's own documents at the moment someone asks. And because the answer traces back to a specific passage, it can cite that passage, so a person can open the source and check it before acting on it. That is the difference between an answer you can verify and one you have to take on faith.
What RAG doesn't fix
RAG reduces wrong answers; it does not eliminate them. If the retrieved passages are ambiguous, or a question touches something two documents disagree on, the system can still return a confident-sounding hallucination built from a bad or incomplete retrieval. And retrieval cannot fix source material that was wrong to begin with — if a policy page is a year out of date, RAG will retrieve the out-of-date version and cite it faithfully. Getting real value out of it means the underlying documents have to be current and not contradict each other before a system answers from them, which is really grounding done properly rather than a shortcut around it.
Related terms: embeddings, vector search, and grounding. If you're weighing whether a RAG-based system fits a question your team keeps re-answering by hand, our knowledge systems page shows what we build — or book a free 30-minute strategy call and we'll look at your documents with you.