You write a detailed design doc. You paste it into your AI assistant. You wait. The output compiles. Tests pass. And yet — it's not quite what you designed. The auth middleware is in the wrong layer. The error handling pattern differs from the rest of the codebase. The field names don't match the schema. You fix it. Next task, same thing. This happens constantly, and it's not a model capability problem. It's a structural problem in how we communicate design intent to AI. And "write better prompts" is not the solution — it's a band-aid. Why "Better Prompts" Doesn't Scale The instinct when AI misses the mark: add more detail to the prompt. Describe the pattern more explicitly. Give an example. This works — sometimes, for that one task. The problem: design intent isn't a single instruction. It's institutional knowledge accumulated over months. It's the reason two experienced engineers on your team write recognizably similar code without coordinating on every PR.…