AI Monitoring

OpsWatchSim put you on a live dashboard. When drift hit quality and cost, you chose Alert then Rollback — monitoring watches after release, not picking a model from a menu.

Useful monitoring covers several layers:

• System health — latency, errors, timeouts, token use, and cost
• Input health — prompt length, language, topic, and unusual traffic shifts
• Output quality — task success, groundedness, refusal quality, and user feedback
• Safety — harmful content, leaked secrets, or suspicious tool calls
• Business outcome — cases resolved, edits required, conversions, or escalations

Dashboards show patterns; alerts call attention to urgent changes. Sample difficult or risky conversations for human review, while redacting personal data and limiting who can inspect logs.

Connect monitoring back to evals: a real failure should become a safe, repeatable test case. That turns production surprises into protection against future regressions.

• Watching a support bot after a prompt or model change
• Detecting that a RAG index is returning stale documents
• Finding a sudden rise in cost or response time
• Deciding whether to roll back a release

User thumbs-up scores are useful but incomplete. Quietly wrong answers may receive no report, and frustrated users may rate the whole product rather than the model.

Logging everything is not harmless. Minimize stored data, redact secrets, set retention limits, and make access auditable.

Use the Try yourself chooser above. Pick a model, then name one quality metric and one operational metric that could prove your choice still works in production.

For a document bot, write an alert such as: “Notify us if unsupported answers exceed 3% for 15 minutes.”