From AI demo to production: a reliability checklist

Almost anyone can build an AI demo that wows in a meeting. Turning it into something dependable is a different job. Before I call an AI system production-ready, it clears this checklist.

1. Evaluation before launch

You can't improve what you don't measure. A set of representative test cases with expected outcomes tells you whether a change made things better or worse — instead of guessing from a few manual tries.

2. Guardrails on inputs and outputs

Validate what goes in and constrain what comes out. That means handling prompt injection, filtering unsafe content, and making sure the model stays on-task and within scope.

3. Graceful failure

Real systems fail — APIs time out, models return junk. Production AI needs retries, fallbacks, and a sensible 'I'm not sure' path instead of a confident wrong answer.

4. Observability

Log inputs, outputs, latency, and cost. When something goes wrong in production — and it will — you need to see what happened and fix it fast. This is the difference between a system you trust and one you cross your fingers over.