MOSS: A Production Framework for Monitoring and Diagnosing Semantic Failures in Search and Recommendation Systems
Detecting semantic failures in production recommendation and search systems is hard: behavioral metrics lag by days, manual review does not scale, and most LLM evaluation frameworks are designed for offline, fixed datasets rather than continuous operational monitoring. We present Model Sanity Service (MOSS), a production framework that addresses this operational gap by embedding LLM-based relevance scoring into a recurring monitoring loop with targeted sampling, strategy-aware prompting, structured alerting, and diagnostic follow-up prompting that categorizes failures into actionable root causes. MOSS has been running in production at a large U.S. retailer for over six months across recom...
| Authors | Alina Nesen, Prathyusha Kanmanth Reddy, David Relyea et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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