Testing verifies that an AI system works as expected before it goes live — checking accuracy, behavior, edge cases, and failure modes across representative scenarios. It includes unit testing of individual components, integration testing of full workflows, and user acceptance testing against realistic inputs.
The gap between controlled testing conditions and real-world variability means testing needs to be genuinely representative, not just confirmatory. An AI that performs well on the clean test cases but fails on edge cases will fail in production — because production is full of edge cases. The goal of testing is to find those failures before users do.
