Feedback Collection

The process of collecting, combining, and organizing feedback from multiple users, evaluators, or automated systems to identify trends, measure performance, and guide AI system improvements.

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.

An ongoing process of using evaluation results, feedback, and operational insights to iteratively improve AI system performance and quality.