AI model regressions: how to catch them after a swap

Silent AI model regressions destroy user trust faster than obvious bugs. Learn how to catch AI model regressions after a swap before they tank your app rating. When replacing an old AI, the main demo might look perfect while hidden edge cases fail. Product managers must look beyond the happy path to prevent silent failures. Swapping a large model for a smaller one might save money, but it can introduce subtle tone shifts or miss critical intents like refunds. To prevent this, record a baseline of old model outputs and run the exact same prompts through the new model behind the scenes. Build a golden dataset of real user prompts to run shadow evaluations and set hard gates that block releases if critical metrics drop. In this lesson: - Recording baseline outputs from your old model - Building a golden dataset for hidden edge cases - Running shadow evals before touching real users - Setting hard gates to block failing releases U2xAI Academy - AI skills for product managers.