AI benchmarks & evals
Also known as: AI benchmarks
What is AI benchmarks & evals?
Structured tests that measure how well a model or an AI application performs. Public benchmarks compare models on shared, standardised tasks; evals are the tests you write for your own use case. Vendor benchmark scores make good headlines but rarely predict how a system does on your data. Your own evals are what count.
Why it matters
The practical reason to build your own evaluations is plain. Without them you cannot tell whether a change helped or hurt. Swap the model, edit a prompt, add a new document source, and quality can move either way with nothing on screen to warn you. An eval set, a collection of real cases with known good answers that you run on every change, turns that guesswork into a number you can track. It also catches regressions, the fix that breaks something which used to work. Public benchmarks cannot fill this role: they measure generic tasks, and their questions tend to leak into training data over time, inflating scores in a way that says nothing about your problem. Building evals is tedious work, and it is what separates a system you can trust from one you only hope is fine.
In practice
A quality lead assembles 200 real questions with agreed-good answers and runs the whole set every time the system changes. A prompt tweak meant to improve tone drops accuracy on one category of queries, and the eval catches it before release. Vendor leaderboards had ranked the underlying model highly, but only the team’s own tests, on its own data, revealed the regression that actually mattered.
