Closing the Loop: Production Drift Detection for LLM Evals
Originally published on AI Tech Connect. What you need to know Drift happens with zero code changes. A provider updates the model behind a stable alias, real traffic diverges from your golden set, the surrounding produc
Originally published on AI Tech Connect.
What you need to know Drift happens with zero code changes. A provider updates the model behind a stable alias, real traffic diverges from your golden set, the surrounding product changes what the model sees, or a tool dependency shifts. Continuous monitoring is a different discipline from CI evals. CI catches regressions your team introduced; production monitoring catches regressions that happened to you. An LLM-as-judge scoring sampled live traffic is the practical backbone β but the judge itself needs periodic recalibration, or it drifts quietly alongside the system it is grading. A statistical test, not a glance at a dashboard, is what separates a real regression from ordinary sampling noise. Golden sets rot. The fix is a standing process that pulls real production incidents back intoβ¦
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.