Feedback Aggregation
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.
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Related terms
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.
Explicit FeedbackFeedback 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.
Implicit FeedbackFeedback 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.
Human FeedbackInformation, 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.
Continuous ImprovementAn ongoing process of using evaluation results, feedback, and operational insights to iteratively improve AI system performance and quality.