Monitoring AI in Production

AI in production degrades. Models change, data drifts, edge cases accumulate, and user behaviour evolves. Ongoing monitoring is not optional.

What to monitor

Quality metrics. Automate quality sampling — use LLM-as-judge to score a random sample of production interactions daily. Track these scores over time and alert on degradation. Metrics should reflect what actually matters to users, not just technical performance.

Failure modes. Log cases where the model refused a valid request, produced an error, failed to follow format requirements, or was flagged by users. Review these regularly and update your system prompt or evaluation set accordingly.

Latency and cost. Token usage, API latency, and error rates should be tracked in your standard infrastructure monitoring. Latency spikes and cost anomalies are early indicators of integration problems.

User behaviour. Are users regenerating responses frequently? Abandoning mid-conversation? These behavioural signals are often the most sensitive indicators of quality issues.

Model version changes

API providers update models, sometimes with breaking changes to behaviour. Subscribe to provider changelogs, test against your eval set before migrating to new model versions, and consider pinning to specific model versions for production stability.

Feedback loops

Build mechanisms for users to flag poor responses. This creates a stream of real failure cases that should be reviewed, used to update your evaluation set, and — over time — inform system prompt improvements.

Monitoring an AI system is not the same as monitoring traditional software. The system can technically succeed (receive a response, parse it correctly) while the actual quality degrades substantially. Quality monitoring requires application-level telemetry, not just infrastructure metrics.