Offline accuracy is a promise; online A/B testing is the proof. Here is how to run experiments that tell you whether a model actually moves the business.
Binate AI
January 5, 2026
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A model that scores higher offline can still lose online — because users behave differently than your test set, or the metric you optimized is not the metric that matters. Online A/B testing measures real impact on a real business metric with real users.
Pick one primary metric, compute the sample size for the effect you care about, randomize at the right unit (user, not request), and pre-register guardrail metrics so a "win" doesn't quietly break something else.
Action Checklist
0/5Peeking and stopping early inflates false positives. Novelty effects fade. Sample-ratio mismatch signals a broken split. Treat the experiment with the rigor of a clinical trial, because the decisions are just as expensive.
One habit silently ruins A/B tests.
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We design experimentation so model changes ship on real, defensible impact.
Assess your AI readinessOffline gets you a candidate; online A/B testing earns the launch. Pre-register, power it properly, and resist the urge to peek.
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