Human Preference Annotation

Human preference annotation is the process of collecting human judgements, typically choosing between two model outputs or rating quality on a scale, to measure subjective dimensions of agent quality that automated metrics cannot capture, such as tone, helpfulness, and trustworthiness.

Annotation data is used to evaluate agents, fine-tune models, and calibrate automated scoring systems.

Closely related concepts include Human Evaluation, Agent Grading Rubric, Hallucination Rate, LLM-as-Judge. Each is defined in the Prefactor glossary.