Grounding is the practice of forcing an AI to answer only from the context retrieved for the current question, and to say it does not know when that context is missing or insufficient. It is the discipline that turns a chatty model into a useful support agent.
Without grounding, the model is happy to fill any gap from its general training. That is fine when you ask about the capital of France. It is dangerous when you ask about a customer's contract terms.
Why grounding is the harder half of RAG
Retrieval gets the right snippets in front of the model. Grounding gets the model to actually use them. The two are often confused, and many systems do the first well and the second badly.
A grounded model treats the retrieved context as the ceiling of what it can claim. If the context says "thirty days for returns", the model says thirty days. If the context says nothing about returns on opened items, the model says it does not have that information. It does not extrapolate. It does not blend in what other companies usually do. It does not assume.
This sounds restrictive. It is. That is the point.
How grounding is enforced
Grounding is enforced through prompting (telling the model explicitly to stay within retrieved context), system design (separating "what the user asked" from "what we know"), and evaluation (testing the model on questions where the answer is intentionally absent from context, to see whether it hallucinates or refuses).
Models vary in how naturally grounded they are. Newer frontier models are better than older ones, but none are perfect by default. Engineering work is needed on top.
What ungrounded AI looks like
An ungrounded AI feels helpful in a demo and unsafe in production. It answers every question. It rarely escalates. The replies sound polished. And once a week or once a day, depending on volume, it confidently tells a customer something that is not your policy. By the time the support manager sees it, the customer has already taken action.
In Keloa
In Keloa, grounding is a core safeguard. The AI agent is instructed to answer only from your knowledge base, to provide citations for every claim, and to escalate when retrieval comes back thin. See how the AI works for the full pipeline.