The stochastic challenge Traditional software is predictable: Input A plus function B always equals output C. This determinism allows engineers to develop robust tests. On the other hand, generative AI is stochastic and unpredictable. The exact same prompt often yields different results on Monday versus Tuesday, breaking the traditional unit testing that engineers know and love. To ship enterprise-ready AI, engineers cannot rely on mere “vibe checks” that pass today but fail when customers use the product. Product builders need to adopt a new infrastructure layer: The AI Evaluation Stack. This framework is informed by my extensive experience shipping AI products for Fortune 500 enterprise customers in high-stakes industries, where “hallucination” is not funny — it’s a huge compliance risk. Defining the AI evaluation paradigm Traditional software tests are binary assertions (pass/fail). While some AI evals use binary asserts, many evaluate on a gradient. An eval is not a single script; it is a structured pipeline of assertions — ranging from strict code syntax to nuanced semantic checks — that verify the AI system’s intended …