One signal

Many AI product teams start by building metrics before identifying the failures they actually need to fix, according to a new guide published by Lenny’s Newsletter. The authors say that error discovery should come first, drawing on work with more than 50 AI companies.

Why it matters

The practical advice is pleasingly unfashionable: find recurring, consequential mistakes in real product behaviour, then design evaluations around those failures. A dashboard built first may still look impressive, but it can end up measuring what is easy rather than what matters. That makes this a useful counterpoint to the growing appetite for ever more evaluation tooling. Before buying another scoreboard, teams may need to ask whether they have found the right game. Should AI teams prioritise discovering real-world failures before building formal metrics, or is early measurement still the best way to find those failures?

Discuss: Should AI teams prioritise discovering real-world failures before building formal metrics, or is early measurement still the best way to find those failures?

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