A/B testing for models compares multiple deployed versions to determine which performs better with real users. Setup: Split traffic randomly between versions A and B, measure business-relevant metrics, run until statistically significant. Metrics to compare: User engagement, conversion rate, task completion, satisfaction surveys, downstream business metrics. Not just model accuracy. Statistical rigor: Power analysis for sample size, significance testing (t-test, chi-square), confidence intervals, watch for multiple comparison issues. Duration: Run long enough for significance and to capture time patterns. Too short may miss weekly cycles. Traffic split: Often 50/50 for speed, but can use 90/10 for safety (test with minority). Guardrail metrics: Safety metrics that must not degrade (latency, errors, safety violations). Halt if violated. Multi-armed bandits: Adaptive approach that shifts traffic toward better-performing variant during experiment. Segmentation: Analyze results by user segments, may find variant works better for some users. Infrastructure: Feature flags, traffic routing, metric collection, experiment management platform. Documentation: Record hypothesis, results, decision, learnings.