RAG explained: how AI answers from your knowledge base

A language model on its own only knows what was in its training data — not your prices, your opening hours or your internal policies. If someone asks about those, the model will, if necessary, fill the gap with a plausible invention. This is exactly the problem RAG solves: retrieval-augmented generation. This article explains, in plain terms, how RAG works and why it is indispensable for a trustworthy assistant.

What RAG means

RAG combines two steps. First the retrieval step: for a user question, the system finds the most relevant passages from your knowledge base. Then the generation step: the language model writes an answer — but only based on the passages it found, not from memory. So the question isn’t “what does the model know about this?” but “what do your documents say about this?”. You’ll find a fuller definition in the glossary under RAG.

Why grounding and citations beat guessing

When a model writes freely, every answer sounds equally confident — including the wrong ones. These convincingly delivered inventions are called hallucinations. RAG reduces them dramatically because the model is tied to concrete passages. The second benefit: citations. Grounded answers in Kyros cite where each statement comes from — with clickable footnotes back to the original. That builds trust and makes answers verifiable instead of something you simply have to believe.

And if the answer is missing?

A well-implemented RAG system doesn’t guess when the knowledge base has no answer — it honestly says it doesn’t have the information. For users, a clear “I don’t know” is far more valuable than an invented answer that causes trouble later.

Golden answers: the fine control

Some questions are too important to leave to the chance of a text search — prices, return policies or legally sensitive topics. That’s what golden answers are for: hand-written model answers that take priority. You decide that a particular question is answered exactly the way you want — word for word. More on this in the glossary under golden answer.

Keeping content fresh

RAG is only as good as the content behind it. An outdated knowledge base delivers outdated answers — with full confidence, because they come from your own documents. Maintaining the content is therefore part of running an assistant: re-crawl URLs when pages change, adjust golden answers when policies change, and review chat logs for questions that had no good source. Handily, in Kyros a knowledge base is reusable and can be assigned to several assistants — you maintain the content once, and every connected assistant benefits.

Conclusion

RAG turns a general language model into an assistant that knows about your business — traceably, with sources and without guessing. Combined with golden answers and well-maintained content, the result is an assistant your customers and your team can genuinely trust.