A/B Testing (AI)
A controlled experiment comparing the performance of two or more AI model variants on live traffic to determine which performs better on specified metrics.
In plain language
Running two versions of an AI simultaneously to see which performs better. Half your customers see AI version A and half see version B, then you compare results to pick the winner.
Why this matters
From an implementation standpoint, A/B testing is how you prove an AI change is genuinely an improvement before it reaches every customer. Releasing a new model to a small slice of live traffic first contains the blast radius if it underperforms. The metrics you choose to compare, not just accuracy but business outcomes, fairness across customer groups and the cost of different error types, become part of your governance record. Treat each test as a documented, reversible release gate rather than an ad-hoc experiment.
Relevance
ImplementationA/B testing belongs to how AI gets shipped safely. It gives delivery teams a controlled, reversible way to release a model change and measure its real effect on live traffic before any full rollout.
Related terms
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