Evaluation-driven development keeps AI roadmaps grounded in real user outcomes.
Why this matters
Without a consistent evaluation loop, teams chase anecdotal bugs and overfit to edge cases.
A practical loop
- Define a small benchmark set from real user workflows.
- Score outputs on a repeatable rubric.
- Compare changes before shipping to production.
- Promote only the changes that improve core metrics.
Keep the scope tight
Start with one high-value workflow. A small but stable evaluation loop beats a broad framework that no one maintains.