#monitoring

Practices and tools for monitoring, tracing, and debugging AI systems in production, covering token metrics, latency, response quality, costs, and hallucination detection.

Practices for configuring effective alerts that notify real problems without generating fatigue from excessive notifications.

Collection and visualization of numerical system measurements over time to understand performance, detect anomalies, and make data-driven decisions.

Ability to understand a system's internal state from its external outputs: logs, metrics, and traces, enabling problem diagnosis without direct system access.