A/B Testing AI Features
Offline evaluation tells you a system should be better. A/B testing tells you it actually is better for real users. This article covers experiment design for AI features: proper randomization, choosing metrics that matter, guardrail metrics that must not regress, sample size planning, and common pitfalls that invalidate results.
Quick Reference
- →A/B test = randomized controlled experiment: users are randomly assigned to control (old) or treatment (new)
- →Primary metric: the user behavior you want to improve (task completion, satisfaction)
- →Guardrail metrics: safety, latency, cost — must not regress even if primary improves
- →Sample size depends on the minimum detectable effect and baseline conversion rate
- →Run for at least 1-2 weeks to capture weekly usage patterns and avoid novelty effects
- →Never peek at results before the planned analysis time — it inflates false positive rates
Experiment Design: Control, Treatment, and Randomization
An A/B test compares two versions of your system: the control (current production system) and the treatment (the new version). Users are randomly assigned to one group, and you measure the difference in key metrics. Random assignment is critical — it ensures that any difference in metrics is caused by the system change, not by differences in the user populations.
If you randomize per-request, a single user might see both versions in the same session — creating a confusing experience and contaminating your results. Randomize per-user (using a hash of user ID) so each user consistently sees the same version throughout the experiment.