Running LLM jobs over hundreds of items, the obvious shortcut is to collapse execution and verification into one model pass: one call, one output, ship it. It fails at scale, and a scoring system for 146 countries across 11 categories shows exactly why. Each score runs 0 to 100 on a single canonical dataset, the overall rating is the arithmetic mean of those 11, and there are no per-country exceptions. One yardstick, applied identically everywhere. Ask a cheap model to generate all 146 in ...