Continuous AI 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.
ELI5
Continuous AI evaluation means testing an AI system repeatedly after it has been released, not just once before launch. New failures, user corrections, policy changes and model updates become new test cases.
For example, if a support assistant gives a harmful answer in production, the team can add that case to a lasting test set and check every later version against it. The test history should be versioned so new checks do not silently erase older lessons.
