Evals, short for evaluations, are structured tests that measure how well an AI system performs on real tasks. They can be a set of known questions with expected answers, a rubric scored by people, or a second AI model acting as a judge. Good evals use your data, not a vendor’s.
Why leaders should care
Without evals, you’re judging AI by vibes, usually from a great demo. Evals tell you whether a change helped or hurt, whether a cheaper model is good enough, and whether quality is drifting over time in production. They turn AI from a magic trick into something you can manage.
Example
A team tests 13 ways to extract tables from the same PDF. Some nail it. Some fail on tables without borders. Some are fast and expensive, others slow and free. Without that side-by-side test, they’d have picked whichever tool had the best demo.
My take
If you can’t measure it, you’re still in demo mode. Build a small eval set from your own messy, real-world examples before you pick tools or models, and keep running it. And when a metric suddenly looks perfect, get suspicious: once a score becomes the target, it stops telling you the truth.
Leave a Reply