The Benchmark to Production Gap: Frontier Evaluations for Adaptive Personalization

AI teams can evaluate hundreds of ideas offline but deploy only a handful—and conventional metrics often select candidates that fail in production. In this talk, we show how production-specific frontier benchmarks, randomized exposure data, segment-level replay, and rank-sensitive evaluation improved offline–online correlation from approximately −0.4 to +0.8, helping select segment-adaptive policies that ultimately lifted platform time spent by 4.5%.