A/B Testing

A/B testing for LLM systems randomly routes requests between two configurations — model versions, prompt variants, or inference parameters — and statistically compares quality and cost metrics across the groups. Unlike canary releases, which are sequential, A/B tests run both variants simultaneously to isolate the effect of a single change. Valid A/B tests require sufficient sample size to detect meaningful differences and must control for confounders such as query difficulty distribution across groups.