For Heads-of · Practitioner
A/B Testing
An experimental method comparing two versions of a system (A and B) on a random subset of users to determine which performs better.
- A/B testing
- experimentation
- evaluation
- optimization
Application to AI
Compare a new model version against the existing one, or test different prompts, features, or configurations to optimize system behavior.
Statistical Rigor
Proper A/B testing requires sufficient sample size to detect meaningful differences, accounting for multiple testing and temporal effects.
Governance Value
A/B testing provides empirical evidence that new versions improve outcomes, supporting decisions about deployment and rollout. It also detects regressions before full deployment.