AI assistants fail in different ways from traditional software. The same question can produce different answers, small wording changes can change outcomes, and new model versions can fix one problem while creating another.
Testing therefore needs to be structured, repeatable and continuous, not a one-off demo before launch.
Start with a test set
Build a versioned set of test cases that covers the intents your users actually have, the edge cases you worry about, and adversarial inputs that try to push the assistant off course.
Make it continuous
Run the test set on every model, prompt or data change. Track results over time, and review failures with domain experts so fixes target the real cause.
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