From prototype to production
A simple framework for shipping AI features safely.
The gap between prototype and production is rarely model quality alone. It is evaluation coverage, monitoring, rollback plans, and operator trust.
Use this framework as a checklist before you expand access beyond a pilot group.
Define the pilot boundary
Choose a limited user set, dataset scope, and set of tools the feature may call. Write down what success means in two weeks and in two quarters.
If the boundary is fuzzy, you will not know whether you are learning or gambling.
Ship with kill switches
Feature flags, rate limits, and model routing controls let you mitigate incidents without redeploying the world.
Practice a rollback once so the team knows the steps when pressure is high.
Expand on evidence
Scale usage when metrics and sampled reviews show stability, not when enthusiasm peaks after a launch demo.
Evidence-driven expansion protects customers and protects the team from burnout chasing reactive fires.