When Public AI Benchmarks Aren't Enough (Part 3 of 3)
Public benchmarks can tell you a great deal about AI model capability. They still may not tell you which model will perform best on the work you actually need done.
This final post in a 3-part series shows how to investigate that gap with a small personal evaluation: a representative task, controlled user-visible conditions, and a rubric written before testing. It also examines what happened when I applied that method to three model systems.