RAG stands for retrieval-augmented generation. It is the technique that lets an AI agent answer questions using your own knowledge, not just what the language model learned during training. Before generating a reply, the system retrieves the most relevant snippets from your sources, then asks the model to write an answer using those snippets as context.
It is the difference between an AI that guesses based on the internet and an AI that quotes your actual returns policy.
Why RAG matters for support and sales
Large language models are trained on a wide slice of public text. They are confident speakers of every topic and authoritative on none of yours. Without retrieval, ask one about your refund window and it invents a plausible number. With retrieval, it reads your refund page and gives the real one.
For customer support, this is the only safe way to use AI on real conversations. Your answers need to match your policies. Your policies live in your help center, your product documentation, your contracts, your past tickets. RAG is the bridge from "the AI sounds good" to "the AI is correct".
How it works in practice
A retrieval system splits your sources into small chunks and stores them in a way that lets the AI find the most relevant ones for any question. When a customer message arrives, the system searches those chunks, ranks the top matches, and passes them along with the question to the language model. The model writes the reply using the retrieved context, ideally with a citation pointing back to the source.
If your help center changes, you re-index and the AI uses the new version. There is no model retraining. You update one document, the AI updates with it.
Limits
RAG is not magic. If a source is wrong, the AI repeats the wrong answer. If a source is missing, the AI either says it does not know or, in worse implementations, fills in the gap. The quality of a RAG system depends on the quality of the index, the retrieval, and the ability of the model to stick to retrieved context.
In Keloa
In Keloa, RAG is the core of every reply. We index your knowledge base, ground the model in retrieved snippets, and ask it to provide citations so you can check the source. See how the AI works and our security posture.