Groundedness

Groundedness measures whether an AI system's output is supported by its source material.

A grounded response makes only claims that can be traced to the provided context: documents, tool results, retrieved data; an ungrounded response introduces unsupported claims, the precursor to hallucination. It is scored 0 to 1, typically by an LLM judge comparing each output claim against the source. Groundedness matters especially for agents because tool results count as context too, and multi-step agents compound ungrounded claims from one step into the inputs of the next.

Closely related concepts include Answer Faithfulness, Answer Relevance, Context Precision, Hallucination Rate. Each is defined in the Prefactor glossary.