What makes a good regression set for an AI workflow?

Example Answer

The way I'd think about it is this: A good regression set is small enough to stay legible and large enough to protect the product from repeating known mistakes. I want it built from real failures, high-value workflows, and cases that historically break when we change prompts, models, or tool contracts.

It should also be intentionally diverse. Not random variety for its own sake, but coverage across user intents, risk levels, customer segments, and failure modes. If every case is a clean happy path, the regression set becomes theater.

I also like each item to have a reason to exist. If a case is in the suite, I should be able to explain what risk it guards against and what good behavior looks like. That keeps the set from turning into a graveyard of anecdotes nobody wants to maintain.

Common Poor Answer

A weak answer is saying a good regression set is just your biggest benchmark. Bigger is not better if the cases are redundant or no longer tied to real product risk.