AI regression testing turns past tasks and failures into repeatable checks. After a model, prompt, tool or workflow changes, the system reruns those cases and compares quality, actions, safety and outcomes with an accepted baseline.
Regression tests protect known behavior but do not discover every new failure. A useful suite grows from real traces, remains representative of current work and separates genuine improvement from changes that merely fit a narrow test set.
ELI5
Regression testing reruns checks for behavior that worked previously after an AI model, prompt, tool, or workflow changes. It helps reveal when an improvement in one area accidentally breaks an accepted result somewhere else.
For example, after updating a support agent, a team can rerun cases based on earlier failures and compare answers, actions, safety, and cost with the accepted version. The suite should keep growing because known cases cannot reveal every new problem.
What belongs in an AI regression test?
It should include a stable task, inputs, environment assumptions, expected constraints and a scoring method for the resulting actions or outcome.
When should AI regression tests run?
They should run when models, prompts, tools, policies or workflow code change and regularly enough to detect drift in production behavior.
