#testing

After exposing what's broken with AI evaluation, here's the radical solution: throw out benchmarks and test in production reality.

Introduction Shipping broken content is a costly mistake. A seemingly minor glitch can lead to lost revenue, damaged brand reputation, and frustrated users.

Traditional testing approaches catastrophically fail for multi-AI systems. I've watched teams spend months on test suites that caught zero production failures.

Current AI evaluation approaches are built for software, not systems that reason. Here's the infrastructure we actually need.

"How can we possibly test features that are built in hours?" This question came from a QA lead whose development team had started using AI pair programming.