Phase 7: Production Observability & Incident Response · 45 min · Langfuse · Arize Phoenix · Python

Quality Monitoring at Scale

One bad score is noise. The same drop sustained for 15 minutes across the same route is a real signal — the two require completely different responses.

Real-world application: Knowing the canonical sustained-drift threshold (a few points, held for 15-60 minutes) instead of alerting on every noisy sample is the specific statistical judgment that separates production-grade quality monitoring from a dashboard nobody trusts.

What you will learn

Explain why sampled production traces should be scored against the same rubric that gates CI
Distinguish a single noisy quality score from a sustained rolling-mean drift
Apply the canonical drift threshold: a 2-5 point drop sustained over 15-60 minutes
Explain why drift detection must be scoped per-route and per-prompt-version, not as one global average

Introduction

Quality Monitoring at Scale

Objective

Bucket a stream of per-minute quality scores into time windows, compute per-bucket means, and flag genuine drift only when a sustained run of consecutive buckets breaches the canonical threshold.

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