How I keep LLMs on a tight leash and stopped hand-creating 30 GitHub issues in t

This one sits at 57% reliability — a developing story, single-sourced from a Dev.to post scored at 6.2, which puts it in background territory. The original account comes from one developer's first-person write-up on Dev.to. Read it there before forming strong opinions about the workflow.

A developer got tired of the cognitive tax that comes with keeping a language model useful. Not the obvious problems — hallucinations, wrong syntax, confidently incorrect APIs — but the slower, more insidious one: the model drifting, losing context, doing whatever it felt like unless you held it by both shoulders and stared it down every session. The fix they landed on, published on May 17th, was a structured constraint system that funnels LLM output directly into GitHub issues rather than letting it sprawl across chat threads that nobody will ever read again. Thirty issues, hand-typed, gone. The model now populates them. The developer decides what ships.

If confirmed as a reproducible workflow rather than a one-person configuration miracle, here is what this means. The real bottleneck in vibe coding has never been the model's ability to generate code — it's been the human overhead required to turn model output into something a project can actually track. Issue creation sounds trivial until you've done it thirty times in a sitting, and anything that moves that work onto the model without sacrificing the human's decision-making role is genuinely useful. Second-order effect: teams that crack this pattern stop treating LLMs as conversation partners and start treating them as structured output machines with a chat interface bolted on. That's a more honest relationship, and probably a more productive one.

Watch for other developers posting variations of this constraint pattern — particularly anyone who has wired it into team workflows rather than solo projects, which would tell us whether this holds under the messiness of collaborative codebases.

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