Continuous Improvement
Also called: Iterative Improvement
An ongoing process of using evaluation results, feedback, and operational insights to iteratively improve AI system performance and quality.
Explore more about Feedback Loops
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.
Feedback AggregationThe process of collecting, combining, and organizing feedback from multiple users, evaluators, or automated systems to identify trends, measure performance, and guide AI system improvements.
Continuous EvaluationAn evaluation approach that continuously measures AI system performance throughout development and production to detect regressions and ensure quality.
Preference OptimizationThe process of optimizing an AI system to produce outputs that better match preferred responses, behaviors, or outcomes.
Active LearningA machine learning approach in which a model selectively requests labels for the most informative data samples to improve performance with minimal annotation effort.