What does a 3,000-dimensional LLM judge evaluation actually look like? Here's a real-world example: 30x100 complex judge evaluations against a single data point. The target is an AI system that creates employment contracts. The driving question was: "Are the clauses in this contract actually enforceable across the globe?" This is not a yes/no question. Every evaluation needs its own judgement call. Once you run a comprehensive evaluation stack, things change from "individual tests" to more like mapping the risk surface. When teams don't get value out of LLM judges, it usually comes down to one of these root causes: a) The judges not calibrated accurately enough. (More on this later.) b) The evaluation stack is too narrow - addressed here. Check the 2 min video showing how it works. The stack created and orchestrated with @ScorableAI CLI. In other domains - if this is not yet standard operating procedure across your high-stakes business questions, or your agent behavioral analyses, it should be. Btw, any lawyers out there who would like to expand this analysis, I'd love to hear from you.