The short answer
Conventional monitoring catches things that break. AI systems more often get worse without breaking, returning successful responses that are less useful than they were.
You need signals that reflect quality, not just availability.
What to watch
| Signal | Catches |
|---|---|
| Correction and retry rate | Output quality falling |
| Abandonment mid-task | Users giving up |
| Output length distribution | Behaviour shifting |
| Refusal or fallback rate | System declining more often |
| Input distribution | Users doing something new |
The last row is a leading indicator. When the inputs change, quality frequently follows, and noticing early gives you time.
Run your evaluation set continuously
- Run the evaluation set against production regularly, not only at deploy.
- Alert on a drop rather than reviewing it manually.
- Keep the results over time so drift is visible.
- Break results down by case type.
- Investigate a drop before shipping anything else.
Providers change models underneath you. Continuous evaluation is how you notice that rather than hearing it from a customer.
Log enough to investigate
- The inputs and retrieved context actually used
- The model and version at the time
- The output returned
- Timing for each stage
- An identifier the user can quote
Without the retrieved context most investigations stall, because the output usually turns out to be reasonable given what the system actually saw.
Alert on the things that matter
Alerting on every anomaly produces noise that gets ignored. Alert on sustained changes in correction rate, refusal rate and evaluation score, and review the rest on a schedule.
Each alert should say what to look at first. An alert that only says something changed wastes the response.