What is continuous artificial intelligence evaluation?
Definition
Continuous artificial intelligence evaluation keeps evaluation active throughout the life of an AI system. New production cases, expert corrections, policy changes and model updates are used to refresh test data, criteria and monitoring rather than relying on a fixed pre-release benchmark.
A continuous loop can retrieve similar prior cases and current guidance for each output, apply relevant checks and send uncertain results to experts. Updates should be versioned and validated so recalibration improves coverage without silently discarding earlier evidence.
Acronyms and aliases
continuous AI eval variantcontinuous AI evaluation variant
General terms
Related terms
Frequently asked questions
Why must AI evaluation continue after deployment?
Real use reveals new failure modes, while models, users, data and domain standards can change after the initial evaluation.
What feeds a continuous AI evaluation loop?
It can use production outputs, expert labels, prior corrections, similar cases, current guidance, incidents and model-version changes.
