How do we quantify hallucinations without a massive labeling team?

Start with automated evals like RAGAS faithfulness and answer relevance on a small, curated set of golden questions. Augment with lightweight human review (50–100 samples/week). Publish pass rate as a metric (`ai_eval_pass_ratio`). Use heuristics (unsupported claims, out-of-context citations) as additional flags, but gate rollouts on the golden-set metrics.

Which tools should we pick: Prometheus/Grafana or Datadog?

If you already run Datadog, use it—tags and notebooks are handy. If you’re Kubernetes-native with SRE muscle, Prometheus/Grafana gives you control and lower cost. The important part is consistent labels and having both metrics and traces (OpenTelemetry) wired through the same IDs.

How do we detect drift in unstructured text?

Compute PSI on token/term buckets and monitor embedding centroid shift (cosine distance) relative to a rolling baseline. Evidently AI can produce drift reports you can convert into metrics. Alert on sustained deviation, not single spikes.

What about privacy? Can we log prompts and outputs?

Redact PII at the edge (phone, email, SSN, customer IDs). Store only hashed identifiers. Keep a sampled, heavily redacted corpus for evals with strict access controls and TTL. Never log raw credentials or secrets; verify via automated checks.

How do we prevent runaway costs during incidents?

Track tokens and cost per request. Put a request-level budget cap and a per-tenant daily cap. Alert on change vs 7d baseline. Add circuit breakers that disable expensive fallbacks if SLOs aren’t improving.

Ai-delivery · Dec 5, 2025 · 10 minute read

Stop Shipping Blind: Dashboards That Catch AI Model Rot Before Users Rage

If you’re not measuring drift, hallucination rate, and p95 latency per model version, you’re running an incident farm. Here’s the gritty playbook we use to spot AI degradation before customers do.

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Stop Shipping Blind: Dashboards That Catch AI Model Rot Before Users Rage

Key takeaways

Implementation checklist