Common ML pitfall: trusting external benchmarks over product-grounded evals
yunta_tsai · x · 2026-09-17
A practitioner calls out a common ML engineering pitfall: over-relying on external benchmarks to judge product quality without understanding what a good product means. A good eval should capture the actual product experience; if the engineer training the model can't craft one, they don't understand or care about the problem deeply enough. The takeaway: design evals around product experience, not public leaderboards.
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