Implementing Observability (Logging / Tracing / Metrics)
If you run LLM features in production, no observability is "driving blindfolded." Make visible what happened, how much it cost, and where it failed.
The 3 Kinds to Collect
- Logs: input (summarized/anonymized), output, errors, decision branches
- Traces: one request's path (prompt build → retrieval → generation → post-processing)
- Metrics: latency, tokens/cost, failure rate, refusal rate
Implementation Tips
- Use a correlation ID so you can trace "user action → internal processing"
- Record prompt/output versions (reproducibility)
- Aggregate cost per user/feature
- Don't keep personal info in logs / mask it
Caution
- Align sensitive-data storage with privacy policy
- Changes that cut observability are production risks; don't reduce them lightly
Key Point
"You can't improve what you can't measure." Observability is the premise for all of operations, cost, and quality improvement. Always build it into the first productionization.
