Relevance measures whether an AI output or retrieved context addresses the user's actual request. A relevant answer stays on task and includes information that helps solve the problem.
In evaluation, relevance is often a first-line quality signal. An answer can be fluent, safe, and grounded while still missing the point. For agents, relevance can apply to responses, retrieved documents, selected tools, and planned actions.