#ai-evaluation

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

Poor AI evaluations don't just hurt individual companies. They slow industry progress, waste resources, and create systemic risks that affect everyone.

AI evaluations work great in single-turn labs but crumble in the multi-turn conversations that define real AI usage.

AI evals companies didn't choose PLG by accident. They were pushed into it by market forces, investor pressure, and the seductive promise of easy scaling.

Most AI evals companies built PLG products that can't see how companies actually deploy AI, leading to evaluations that are dangerously wrong.

How to create custom evaluations, model-graded assessments, and domain-specific benchmarks that actually predict real-world performance

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