Hybrid Search A/B Testing — Measure Search Quality Changes

I will build a hybrid search A/B testing framework — enabling controlled experiments comparing search configurations, measuring click-through rate, result engagement, and user satisfaction across configurations, and applying statistical significance testing to separate genuine quality improvements from random variation. Search A/B testing produces more reliable quality evidence than offline evaluation on a static query set — real user behaviour on live search queries reveals quality dimensions that a curated evaluation set misses, particularly for long-tail queries that are underrepresented in typical evaluation sets.

The framework covers traffic splitting for search experiments, result tracking with engagement metrics, user satisfaction measurement, statistical significance testing, segment analysis by query type, and a search quality dashboard with experiment results.