The line about validation being a mandatory second layer resonates, especially your Goal Health example where a good prompt still let the model invent numbers. I have found the same split: prompt quality reduces the error rate, but only a schema check plus fallback actually bounds the blast radius. Do you run those output validators inline per request, or as a separate offline eval pass on sampled traffic?
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.
Kartik N V J K
AI Developer | Making AI reliable, trustworthy & accessible to everyone | Active community contributor
The line about validation being a mandatory second layer resonates, especially your Goal Health example where a good prompt still let the model invent numbers. I have found the same split: prompt quality reduces the error rate, but only a schema check plus fallback actually bounds the blast radius. Do you run those output validators inline per request, or as a separate offline eval pass on sampled traffic?