Explicit Feedback

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.

Data that records preferences between AI outputs or behaviors, typically used to train or optimize models toward preferred responses.

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.

A machine learning alignment technique that optimizes model behavior based on preferences gathered from human evaluators.