Detecting silent degradation: input drift, provider-side changes and feedback-signal dashboards.
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How do you monitor LLM quality drift in production?
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Understand the problem
driftmonitoringproductionquality
02
Attempt it yourself
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Quality can degrade without any deploy: user inputs drift from what you tested, providers update models or infrastructure, and upstream data changes. Monitor continuously: run LLM-judge scoring on a sample of live traffic, track proxy signals (thumbs-down rate, retry/rephrase rate, guardrail and repair-loop triggers, r
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Continuous sampled scoring job
// hourly cron: score a sample of the last hour's traces
const sample = await traces.sample({ lastHours: 1, rate: 0.03, feature: "support" });
for (const t of sample) {
const score = await judge.score("faithfulness", t.context, t.output);
await qualityTs.write({ feature: t.feature, model: t.model, score, at: t.at });
}
// alerting: 7-day rolling mean vs trailing 24h, per feature
// fires when delta > 2σ — catches provider-side shifts with no deploy marker
await alerts.evaluate("quality.faithfulness", { window: "24h", baseline: "7d", sigma: 2 });05
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