Grounding

Grounding is when an AI system anchors its generated response in specific retrieved source material, a live web page, a document, a database record, rather than relying only on patterns learned during training. It's what separates a cited answer from an unsupported one.

Why it matters

A model that isn't grounded on real, current sources is more likely to fall back on stale training data or guess, which is a direct path to hallucination. For a brand, the practical goal is straightforward: be part of what the model actually grounds its answer on, not just something it might vaguely recall.

Related terms

• Retrieval-Augmented Generation (RAG)

  Retrieval-Augmented Generation (RAG) is the architecture behind most AI search: the system retrieves a set of relevant documents or passages at query time, then generates its answer grounded in that retrieved material, rather than purely from what it learned during training.

• Hallucination

  A hallucination is when an AI system generates a plausible-sounding but factually unsupported claim, a wrong statistic, a misattributed quote, a fabricated detail, that isn't actually backed by any of the sources it retrieved when forming its answer.