Grounding

Grounding is the practice of giving a model source material to answer from, and having its answer stay tied to that material, rather than to whatever it happened to learn during training. A grounded answer can point to the passage it came from; an ungrounded one can only be checked by an expert who already knows the right answer.

A regulated decision, a coverage determination, a policy interpretation, a compliance finding, needs a source a reviewer can actually check, not a plausible sentence with nothing behind it. Grounding is what makes an AI-generated answer reviewable rather than something to be taken on faith.

An agent answering a question about a refund policy quotes the exact clause and the exact document it came from, so a reviewer can verify the answer in one click rather than re-researching the question from scratch.

In Dynamiq, Knowledge Base retrieval and GraphRAG both return the source passages or facts an agent used alongside its answer, guardrail validators can check an output's structure before it reaches a user, and evaluation metrics such as Faithfulness and Context Recall score whether an answer actually reflects what was retrieved, rather than drifting from it.