Labeling
Also called: Data Labeling, Annotation
The process of assigning structured annotations, categories, ratings, or ground-truth values to data or AI outputs so they can be used for training, evaluation, validation, or continuous improvement of AI systems.
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Related terms
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.
Golden DatasetA curated and validated collection of high-quality reference examples with trusted labels or expected outputs that serves as a benchmark for evaluating, testing, and monitoring the performance of AI models and applications.
Evaluation DatasetA curated collection of test examples, inputs, and expected outcomes used to measure the quality, accuracy, safety, and reliability of AI models and systems.
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.