Twenty minutes prompting. Three days repairing.
That story is often used as proof that AI writes bad software.
Sometimes it does. Models invent APIs, miss edge cases, and make poor design choices.
But another failure is hiding inside that complaint: the classic tree-swing problem, compressed to machine speed.
The engineer knows they want a swing. The brief asks for a “dynamic, scalable outdoor kinetic relaxation mechanism.” The model fills the gaps with a plausible direction. Nobody challenges the assumption.
Twenty minutes later, the first real integration is also the first real test.
A hallucinated API is a model failure.
A coherent implementation of an unspoken assumption is an agreement failure.
AI did not remove requirements engineering. It removed the waiting that used to expose weak requirements before they became architecture.
The response is not one perfect prompt. It is a governance stack:
• Durable repository instructions that preserve architectural intent
• Specialized agent and workflow files that define the job
• Task briefs with boundaries and acceptance evidence
• Scoped tools and permissions that limit the blast radius
• Hooks, tests, lint, schemas, security scans, and CI
• Human review and release gates for the remaining uncertainty
Instructions steer.
Permissions constrain.
Checks reject.
Humans own the release.
AI is a velocity microphone. It amplifies the signal, the ambiguity, and the consequences.
I wrote about how to keep that speed useful:
https://www.likhansiddiquee.com/blog/velocity-microphone/
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