Why do most intelligent systems fail when they hit production? It’s rarely because of a weak algorithm. Instead, it’s usually a testing framework stuck in a bygone era. If you’re still running “Expected vs. Actual” spreadsheets for non-deterministic models, you’re trying to measure a cloud with a ruler.
The reality is that traditional quality checks create a false sense of security. This leads to failures in live environments. You’ve got to stop testing for a single “correct” answer. It’s time to start testing for the boundaries of acceptable behavior.