When Model Selection Breaks the Product: A Reverse-Guide to Avoiding Costly AI Mistakes

It happened during a Q4 2024 migration of a customer-facing search service named Polaris: the team swapped the inference pipeline without an evaluation that matched production load, and overnight latency spiked, error rates doubled, and the budget forecast moved from "comfortably under" to "alarmingly over." The post-mortem showed the same pattern I see everywhere-beautiful demos, shallow tests, and decisions driven by the wrong signals. This is a reverse-guide: a focused tour of the anti-patterns, the damage they cause in the category of "What Are AI Models," and a concrete safety plan for recovery. Post-mortem: the shiny object that tripped the team What looked like the obvious win was a larger model with better-looking outputs on a few hand-picked queries. The "shiny object" was a new decoder variant that produced more fluent paragraphs in playground tests, and the team assumed this meant it was strictly better. The cost of that assumption: a 3x inference cost increase and a 4× rise