Shipping LLM apps to production: the checklist nobody gave you

Building an LLM prototype is easy. Running one in production is not. The gap between a working demo and a reliable product is where most AI initiatives stall.

In this article we share the production checklist we use on every client engagement — covering evaluations, observability, fallbacks, prompt management, and cost control.

The single highest-leverage habit you can adopt is treating evaluations as first-class artefacts. Without them, every prompt change is a leap of faith and every regression is a customer-reported bug.

We will also dig into cost: most teams overspend on tokens by 2–4× simply because nobody owns the dashboards. A small amount of telemetry pays for itself within weeks.