What is grounding in AI?

Grounding means tying the answer to a source you trust, usually your own document, and showing that source. The model still writes the sentences. It is no longer free to supply the facts. If the source does not contain the answer, the system should say so.

This is the practical defense against a fluent invention. It depends on the search returning the right page, and on the model staying inside it. Check both. An answer with a link to the wrong paragraph is still ungrounded. So is a correct link the user never sees.

Think of it this way: Grounding is what separates a cited encyclopedia entry from a rumor. The encyclopedia states the fact and shows you its source. The rumor sounds just as confident.

A compliance assistant grounded in the company's actual policy documents cites the exact clause and document version for every answer. An ungrounded model produces an answer that sounds right but may not match current policy.

A field technician asks what torque a bolt needs. The system answers from the current work instruction and shows the step. Last month it answered from a retired sheet a technician had uploaded to be helpful. Removing that sheet was the grounding fix. The model did not change.

For any task where accuracy is non-negotiable and a source must back the answer: legal, compliance, medical, financial, and policy-sensitive work. Grounding requires good source documents. If the underlying knowledge base is incomplete or outdated, grounding amplifies that gap. Maintain the source content as rigorously as the AI system itself.

RaftLabs treats this as part of the build: a source on the answer, a test set, and a record of what the system did. The launch is the start of that work, not the end. The related work on our side is RAG development.

This sits with the other reliability & risk terms on the glossary. Why a confident answer can still be wrong, and how you catch it. Worth reading next: Hallucination, Guardrails, and Evaluation (evals).