Observability and Evaluation
Connect traces, prompts, tool calls, quality scores, latency, and cost.
Key takeaways
- AI observability must answer both operational and product questions: did it work, was it useful, what did it cost, and where did it fail.
- The signal map connects request traces, prompt and model metadata, tool calls, retrieval results, user feedback, and evaluation scores.
- Run a review loop: inspect failures and cost outliers, compare quality with feedback, find the cause, ship one controlled change, then re-run evaluation and watch for drift.
- Show quality, latency, cost, and error rate together, since optimizing one metric in isolation often damages another.
AI observability must answer both operational and product questions: did it work, was it useful, how much did it cost, and where did it fail?
Signal Map
| Signal | Use |
|---|---|
| Request trace | Debug latency and failures |
| Prompt and model metadata | Explain behavior and cost |
| Tool calls | Audit side effects and data access |
| Retrieval results | Diagnose grounding problems |
| User feedback | Measure usefulness |
| Evaluation scores | Track quality over time |
Review Loop
- Inspect failures and high-cost outliers.
- Compare quality metrics with user feedback.
- Identify prompt, retrieval, model, or tool cause.
- Ship one controlled improvement.
- Re-run evaluation and monitor drift.
Dashboard Rule
Show quality, latency, cost, and error rate together. Optimizing one metric in isolation often damages another.