Generative models are increasingly used for scientific inverse problems, but their posterior or predictive uncertainty can still be biased or overconfident. This paper introduces Frequentist-Bayes (FreB), a protocol that reshapes AI-generated posterior distributions into locally valid confidence regions with the intended coverage while remaining efficient when training and target distributions agree. The approach is demonstrated on physical-science case studies involving dataset shift, competing theoretical models, and selection effects.