Human Feedback
Also called: User Feedback, Human Input
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.
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Related terms
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 CollectionThe 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.
Feedback PipelineA structured workflow for collecting, processing, analyzing, and incorporating feedback into the evaluation, improvement, and continuous refinement of AI models and systems.
Reinforcement Learning from Human FeedbackRLHFA machine learning alignment technique that optimizes model behavior based on preferences gathered from human evaluators.
Human EvaluationAn evaluation method in which human reviewers assess the quality of AI system outputs against defined criteria such as correctness, relevance, helpfulness, safety, or fluency, providing judgments that complement or validate automated evaluation metrics.