Grounding
Also called grounded generation, source grounding
Making an AI base its answer on specific sources you provide or it retrieves, instead of only its memory.
Grounding means connecting a model's answer to specific, checkable information: documents you paste in, search results, a database, or company files. A grounded answer is built from that material and ideally cites it, rather than relying on whatever the model absorbed during training.
Grounding is the main practical defence against hallucination and outdated knowledge. If the source is in front of the model and the prompt says to use only that source, the model has less reason to invent details, and you can verify claims by checking the citations. Search-enabled chat apps, retrieval-augmented generation systems and "chat with your documents" tools are all forms of grounding.
Grounding works best with clear instructions: answer only from the provided material, quote or cite each claim, and say so when the material does not contain the answer. Without that last permission, models often fill gaps from memory anyway, mixing grounded and ungrounded statements in a way that is hard to spot.
Example
"Using only the policy below, answer: can staff carry over unused leave? Cite the section. If the policy does not say, reply 'Not covered.'" The model answers "Yes, up to 5 days, used by 31 March [section 4.2]".
Common mistake
Assuming citations prove the answer is grounded. Models sometimes cite a real source for a claim that source does not make. Spot-check that the quoted passage really says what the answer claims, especially for anything you will act on or publish.