Your LLM prompts are interfaces. Start treating them like it.

If you've ever debugged a production LLM system by "just rephrasing the prompt," this post is for you. The problem isn't the model. It's the instruction. Most LLM instructions are written the way people write notes to themselves, informally, with shared context assumed, maintained by whoever wrote them. This works for one-off experiments. It fails in systems where instructions are authored once, executed thousands of times, and maintained by teams who weren't there when the original decisions were made. The failure modes are predictable: Context collapse permanent facts, session decisions, and per-task instructions are mixed into one blob. You can't cache anything, you re-send everything, and changing one thing breaks another. Implicit constraints "don't touch the API layer" lives in someone's head or a Slack thread, not the instruction itself. No output contract instructions describe what to do, not what correct looks like. Evaluation becomes subjective. Retry as debugging when output