Trust the parser, not the prompt: what running a cheap LLM in production taught me
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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…
1Key Takeaways
- 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.
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that i run an LLM in the core loop of a small production app: Mening corrects language learners' writing every day.
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