From AI Prototype to Production: An Engineering Checklist

A production AI system needs more than a good prompt. Define measurable acceptance criteria, evaluate representative data, protect sensitive information, constrain tools, monitor quality and cost, establish human escalation paths, and plan model and data changes.

How to put this into practice

Start with a clearly defined use case, measurable acceptance criteria and representative data. Validate assumptions with a small evaluation, then expand the solution with security, governance and operational monitoring built in.

Every implementation should be reviewed against the needs of its users, the risks of its environment and the evidence gathered during testing.