Monitoring › Monitoring

When a language model leaves the lab and enters production, the work has only just begun. Training a model is a bounded problem: you define a dataset, run experiments, and converge on something that performs well on your evaluation suite. Deploying a model is an unbounded problem: real users send inputs you never anticipated, the world changes beneath your feet, and failures can be silent. A model that returned thoughtful answers in testing might start returning rambling non-answers three months later, and without monitoring you would never know.

Monitoring answers a simple question: is the system doing what you expect, right now? That question turns out to be surprisingly hard to answer for language models. Unlike a relational database query that either returns the right row or throws an error, a language model always returns something. It will confidently answer questions about topics it knows nothing about, shift its tone without warning, or gradually degrade in quality as its context grows stale. These failures are invisible unless you are actively measuring for them.

This chapter covers the full monitoring stack for production LLM systems: the metrics that matter, how to collect and store them, how to build dashboards that expose problems, and how to configure alerts that wake you up when something breaks. Along the way, we will look at the specific observability challenges that language models introduce and work through a practical implementation using OpenTelemetry, Prometheus, and Grafana-style visualizations.