Treating AI Prompts Like Jira Stories Changed My Workflow

Been deep in the zone building with AI tools like GPT, Kilo, and Lovable. Pairing them with Vite + Supabase to generate real production software. I’ve got a solid workflow. Clear prompt stacks. Good results. But something’s been bugging me—and it just clicked.

Every AI coding task should be treated like a Jira story.

Because that’s what humans need to start work safely. And AI is no different.

What This Means

Imagine every prompt stack starts with a predefined issue template:

• Bug → Injects: file path, error message, logs, expected behavior
• Feature → Injects: schema refs, RLS, UI interactions, validation rules
• Refactor → Injects: architectural goals, existing drift, test coverage
• Spike → Injects: known unknowns, edge cases, current constraints

Each one kicks off a prompt that already knows the shape of the work. No wasted tokens. No ambiguous context. Just direct instructions, like a safe handoff between teammates.

Why This Matters

It prevents architectural drift. It reduces hallucinations and fragility. It makes AI output safe to commit, review, and ship. It lets me automate pretext and build prompt scaffolds like infrastructure. It gets closer to a world where AI agents can pick up real tasks without babysitting.

This isn’t just about better prompts. This is AI-native software engineering.

What I’m Building

I’m working on a registry of AI Story Types tied to development categories, schema-aware injection, and safe prompt chunking. Treating prompt stacks like reusable, versioned infrastructure.

The pattern is emerging. I’m not sure anyone has fully systematized it yet—at least not in a way that’s publicly documented and repeatable. But that’s the direction I’m moving in.