Every repo has them now: CLAUDE.md, AGENTS.md, a folder of markdown rule files written to steer the AI. Write the rule once, get the code you want. Except these files are advisory, not enforced, as every major vendor says so in their own docs. The research backs it up. Adding them doesn't always improve results, and it can raise the costs by more than 20%. Adherence fades as files grow and conversations run long.
This talk is about how to minimize and fix this problem. First, how the tools really work: how Cursor, Claude Code, and Codex load and rank rule files, why precedence between them is often undefined, and how to check what actually reached the model on a given run. Then how to govern rules like real code: scope each one so it fires only where it should, put the non-negotiables up top where the model pays attention, and prune the files before they rot or start contradicting each other. And for the rules that truly cannot fail, move them out of prose and into hooks and CI, where the action is guaranteed every time.
Do all that, and each rule earns a second job. Beyond steering the AI, a written-down rule is the standard you measure the finished code against. And that gap, between the app you described and the app you got, is where trust actually lives. You'll leave knowing which of your rules do real work and which just sit there looking like they do, and how to verify that instead of assuming it.
This talk has been presented at AI Coding Summit Berlin, check out the latest edition of this Tech Conference.
