Data annotation converts raw examples into evidence with explicit meaning. Domain experts or trained reviewers may label correctness, preference, error type, safety, relevance or another quality dimension needed by an artificial intelligence system.
Annotation quality depends on clear guidelines and reviewer calibration. Self-serve tools can reduce operational cost, but disagreements, uncertainty and ownership should remain visible rather than being hidden behind a single label.
