User Feedback

Feedback that is intentionally provided by users or human evaluators through ratings, preferences, corrections, or written comments to assess or improve an AI system's outputs or behavior.

Feedback inferred from user behavior or interactions rather than explicitly provided ratings or comments, such as clicks, dwell time, task completion, corrections, or repeated usage, which can be used to evaluate and improve AI systems.

Information, evaluations, corrections, or preferences provided by human users or reviewers about an AI system's outputs or behavior, which can be used to improve model performance, refine prompts, optimize workflows, or guide future decisions.

The process of gathering feedback from users, human evaluators, or automated systems to assess AI system performance and provide data for evaluation, improvement, and model refinement.

An evaluation method that assesses system performance and usability through direct observation and structured human interaction.