When testing AI features that don’t always give the same output for the same input, the key is to shift your mindset a bit. Instead of expecting a single “correct” answer, think in terms of patterns or ranges of acceptable behavior.
You can run the same test multiple times and look for trends or statistical patterns rather than exact matches. Using tolerance bands helps you spot outputs that are way off, and anomaly detection can flag anything unusual. Also, make sure to log all the outputs—over time, this will give you a clearer picture of how your AI behaves and where it might need adjustments.