Tying a model’s answer to specific, retrievable sources so the claims inside it can be checked rather than trusted.
Also known as source grounding, evidence grounding, factual grounding
Grounding is the practice of making a model’s output rest on identified source material rather than on whatever it absorbed during training. In a grounded setup the system retrieves relevant documents first, hands them to the model, and asks it to answer from them, usually with citations pointing back to the passages actually used.
Grounding is what separates an answer you can audit from an answer you have to take on faith. It is also the mechanism sitting underneath brand visibility in AI answers: when an assistant cites a source, that source was retrieved and judged usable as grounding material. Which pages get pulled in, and which survive that judgment, has quietly become a distribution question.
Grounding sits underneath two things agencies are already being asked about: whether AI output is safe to ship, and whether a client shows up in AI answers at all.
It is the quality gate on client-facing claims. A grounded workflow pointed at the client’s approved fact base and legally cleared claims produces copy whose every assertion traces to a document. An ungrounded one produces confident sentences that somebody then has to fact-check line by line.
It decides who gets cited. Content that is specific, attributable, and structurally clean is easy to retrieve and easy to ground against. Padded, hedging, keyword-shaped writing is not, which is a large part of why so much SEO-era content performs badly inside AI answers.
It gives measurement something solid to hold. Citations are observable. You can track which client pages get used as grounding across assistants and prompt types, which is a more useful signal than a rank position on a results page fewer people are visiting.
A wellness client will not let AI anywhere near its copy, and the reason is sound: one unsupported efficacy claim is a regulatory problem, not a revision. Grounding is the studio’s way in. It builds a retrieval layer over exactly three sources, the approved claims document, the cleared clinical summaries, and the brand tone guide, and the model drafts only from those. Every sentence in the output carries a pointer to the paragraph it came from. Review time drops sharply, because the reviewer is checking a citation instead of relitigating a sentence.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.