What’s the minimum viable AI observability stack?

OpenTelemetry traces for each stage (retrieval, generate, moderation), Prometheus metrics for quality/safety/latency/cost, Grafana dashboards, and a daily drift job that pushes centroid and PSI metrics. Add a 5-minute synthetic eval to catch regressions between traffic spikes.

How do I measure hallucinations objectively?

Start with a golden test set and LLM-as-judge scoring (exact-match, factuality via retrieval overlap, refusal correctness). Track eval pass rate and a separate irrelevant/unsupported claim rate. Layer in shadow traffic comparisons for challengers.

Won’t guardrails nuke latency?

Inline moderation adds tens of milliseconds for most providers. Make it concurrent with retrieval when possible, and only run heavy checks (e.g., injection classifiers) on risky intents. Measure p95 before and after; if it pushes you over SLO, switch to sampled checks plus a circuit breaker.

How do I avoid leaking PII in traces/logs?

Redact at ingestion. Use a sanitizer middleware that replaces emails/phones/SSNs with hashes before exporting spans/logs. Store raw prompts encrypted and reference by ID in traces. Verify your OpenTelemetry exporter isn’t shipping raw bodies.

Ai-delivery · Oct 24, 2025 · 10 minute read

Dashboards That Catch AI Model Degradation Before Users Do

If your first signal of a failing model is a Support ticket, you don’t have observability—you have vibes. Here’s how to instrument, guard, and visualize AI systems so drift, hallucinations, and latency spikes trip alarms hours before customers notice.

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Dashboards That Catch AI Model Degradation Before Users Do

Key takeaways

Implementation checklist