There is a practical line between AI experimentation and AI engineering, and it is drawn by evaluation. Experimentation asks "does this look good?" Engineering asks "does this meet the specification, across the cases that matter, reproducibly?"
The evaluation loop
Define desired behaviour. Create test cases from realistic work. Run evaluation datasets. Measure output quality. Find failure modes. Improve the system. Deploy. Monitor.
The loop is unglamorous and it is the entire difference. Each iteration produces evidence instead of impressions.
What to evaluate
Correctness against expected output. Behaviour under missing or ambiguous input. Escalation quality — does the system ask for help at the right moments? Regression: did the last change break what already worked?
Evidence compounds
An evaluation suite is an asset. It survives model upgrades, prompt changes and staffing turnover. It is how an AI system stays trustworthy across years rather than demos.