Why is A/B testing the honest test for AI features?
Because randomization removes the alternative explanations. Roll an AI assistant out to everyone and measure the quarter, and you have measured the quarter: seasonality, campaigns and luck included. Split traffic at random and the two groups share all of that, so the gap is the feature. Every vendor lift claim should be read as a hypothesis until your own split test confirms it, which is the discipline AI ROI measurement leans on when the stakes are a contract renewal.
How does A/B testing fit into AI evaluation?
As the final, online stage. Offline, a candidate model or prompt runs against held-out cases, the territory of model validation; online, the survivors meet real users in a controlled split, the step online versus offline evaluation covers in depth. The two disagree often enough to justify both: an output that scores well on a rubric can still lose with customers. Offline tests say a change is safe; only live traffic says it is better.
How is AI changing A/B testing itself?
Two ways. Multi-armed bandits reallocate traffic toward the winning variant during the test, trading some statistical purity for less exposure to the loser, useful when every visit has a cost. And generative tools draft the variants, making it cheap to test five headlines where one was affordable before. The statistics get no more forgiving because AI wrote the variant: sample sizes, significance and a pre-chosen metric still separate a result from a coincidence.
What are the classic A/B testing mistakes?
Peeking and stopping the moment the graph looks good, which converts noise into decisions. Testing five things at once and attributing the result to the favorite. Choosing a metric after seeing the data. And shipping a winner measured on clicks that loses on the number that matters, for an agent that is task completion rate, not engagement. Decide the metric, the sample size and the stop rule before the test starts, then believe the result even when it disappoints.