LLMs in Production: Telemetry, Guardrails, and Failure Modes I Actually Saw
Operational lessons from shipping LLM-backed features, including the telemetry and guardrails that saved us.
The Laptop Trap
LLM prototypes demo well, but production reality introduces latency spikes, hallucinations, prompt regressions, and runaway cost curves. Here is what I instrumented after shipping multiple LLM-powered workloads.
Telemetry You Need on Day One
- Structured logging: Log every request and response with redacted fields and correlation IDs.
- Latency histograms: p50/p95/p99 per model, provider, and feature flag.
- Rate metrics: Tokens per minute, cost per user segment, cache hit rates.
- Dashboards: We built Grafana boards per feature with burn charts tied to budget alerts.
Guardrails That Worked
- Output validators: Regex and schema checks before content hits the UI.
- Content filters: Abuse detection plus allowlists for critical intents.
- Retry with fallbacks: Downgrade to curated responses when upstream is flaky.
- Prompt checksum: Version prompts and tools so we can roll back bad changes.
Debugging Real Failures
Hallucinated Compliance Answers
A compliance assistant invented policy IDs. Telemetry flagged a spike in manual edits. We added a deterministic verification step against the policy database and the issue disappeared.
Latency Spikes From Provider Degradation
p95 jumped from 1.2s to 8s. Correlation IDs showed most calls pinned to a single Azure region. We enabled multi-region routing and fronted requests with a circuit breaker that moved traffic within two minutes.
Designing for Iteration
- Feature flags allow segment-based rollouts of new prompts.
- Prompt repositories (Git + automated tests) keep templates reviewable.
- Offline evaluation harness replays traffic against candidate prompts before deployment.
Takeaways
Treat LLM stacks like any distributed system: observe everything, fail safely, and version ruthlessly. Your future self will thank you when production decides to get weird at 3 AM.