SaysoftWhat RAG actually is (and when you need it)
Retrieval-augmented generation grounds an AI assistant in your own documents. Here's the plain-language version — and when it's worth it.
A plain language model knows a lot about the world but nothing about your company. Ask it about your refund policy and it will guess. RAG — retrieval-augmented generation — fixes that by giving the model your own documents to read before it answers.
The pattern is simple: when a question comes in, the system searches your knowledge base for the most relevant passages, hands them to the model, and asks it to answer using only that context. The result is an assistant that cites your policies, not the internet's.
RAG is worth it when you have a body of trusted content — policies, manuals, past tickets — and people keep asking the same questions about it. It's not worth the complexity for a handful of static FAQs; a plain page does that better.
Done well, RAG comes with guardrails: evaluation so you know answers are accurate, and a fallback to a human when the system isn't sure. That last part is what separates a useful assistant from a confident liar.
