What Building Ten AI Features Taught Us About Reliable AI

Thanks, that's a great distinction and I completely agree. The validation checks discussed here are designed as runtime safeguards, running inline on each relevant request before the output reaches the user. The offline evaluation was used to measure model quality, identify failure patterns and monitor changes over time. The two serve different purposes: runtime validation protects individual requests while offline evaluation helps improve the system. The Goal Health example highlighted why having both layers matters.