Data Curation

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.

The process of labeling, categorizing, and documenting errors in AI system outputs to support evaluation, debugging, model improvement, and feedback-driven training.

A curated collection of test examples, inputs, and expected outcomes used to measure the quality, accuracy, safety, and reliability of AI models and systems.

A 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.

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.