Most teams pick the model first and work out how to test it afterwards. That ord
By Rizwantufail · November 7, 2026 · Curated by George's Blog
Most teams pick the model first and work out how to test it afterwards.
That order causes a lot of avoidable pain. A benchmark score tells you how a model compares with other models on someone else’s data. It says very little about how it will behave on yours, in your domain, when it is wrong.
So I would pick the evaluation method before the model.
Nine questions, and each one needs a different method:
→ What does good look like?
→ Is the answer right?
→ How did it get there?
→ Is it safe?
→ Does it hold on our data?
→ Is it still working?
→ Did the update break it?
→ Is it worth the cost?
→ Can we prove it?
Question three is the one most teams skip. Benchmarks score the final answer. Production breaks on the path taken to reach it, the wrong tool called, a loop entered, an error compounding across steps.
And question six catches the slow failure. Golden datasets age. Judge scores drift.
A test suite nobody has refreshed in six months is measuring a system that no longer exists.
Save this before your next model decision.
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