Skip to solution
hardAI Engineering

How do you monitor LLM quality drift in production?

348 views
01

Understand the problem

Detecting silent degradation: input drift, provider-side changes and feedback-signal dashboards.

driftmonitoringproductionquality
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

Stuck? Beam a request up — the console returns a conceptual nudge that guides your logic without spoiling the implementation.

03

Study the solution

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

Solution ready — 2 min read

Classified // press E to declassify

04

Read the code

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

Join the discussion

Discussion (0)

Sign in to join the discussion.

No responses yet. Be the first to share what you think.

Transmission complete // awaiting log

KEEP THE
STREAK ALIVE.

Dossier 76 of 80 decoded in the AI Engineering track. One more won't hurt.

Back to track