Nexuldra
// ai
Grounding
Grounding is anchoring a language model's answers to retrieved facts — pages from your documents, rows from your database — handed to it at question time, instead of relying on what it memorized in training. The model reasons over your material, cites it, and is instructed to say "I don't know" when the material does not contain the answer.
Grounding matters because it relocates the correctness problem from an opaque model to auditable material: instead of asking "is the model right about our refund policy", you ask "does page 12 say what the bot claimed". It is the most effective countermeasure against hallucination — not a complete cure, since the model can still misread correct material, but it converts an unpredictable failure into one you can test page by page.
In practice, grounding is the RAG pattern plus a hard refusal rule: retrieve the relevant chunks, answer only from them, show the citation, and script the exact sentence for "the material does not say". The rule must be tested with an eval set that includes questions the material deliberately cannot answer — a system that always answers looks excellent until it meets the edges, and edges are where chatbots live or die.
Frequently asked questions
- Is grounding the same as RAG?
- RAG is the mechanism — retrieving documents at question time. Grounding is the broader discipline: answering only from provided material, citing it, and refusing when the material is silent. You can ground answers in a database or an API just as well as in retrieved documents.
- Does grounding eliminate hallucination?
- No — the model can retrieve the wrong chunk or misread a correct one. What grounding gives you is a measured, low error rate with honest refusals, which is usually shippable. The discipline that keeps it shippable is a fixed eval set, rerun after every change to prompt, model or retrieval.
// go deeper
Full treatment in the guide: How to stop a chatbot from making things up