I run an LLM in the core loop of a small production app: Mening corrects language learners' writing every day. Unit economics put the everyday call on a cheap, fast model tier, and the output feeds a database, so it has to obey a contract: strict JSON, a closed set of error categories, explanations in the learner's UI language, no invented "fixes".
The cheap tier is genuinely good at the task. It is terrible at following the rules around the task. This post is about the escalation ladder I climbed before accepting the house rule in the title, with real numbers from the two fights that taught me it.
Fight one: the model that wouldn't stop "correcting" 了
Chinese learners kept getting a specific non-correction: the model would insert 了 into sentences that were already fine, then explain why the learner "needed" it. A native-speaker check said the original sentences were correct.
I climbed the prompt ladder one rung at a time, deploying and watching live traffic after each:
