Vibecoding Works Best When AI Follows a Real Engineering Workflow

I used to think vibecoding meant telling an AI what to build and hoping the output worked. The more useful approach is to combine an AI coding assistant with disciplined software-engineering practices. That means isolating features in worktrees, keeping the application modular, and giving the assistant access to the real development environment: version-control tools, package managers, build tools, logs, tests and database tooling. The most powerful part is visual verification. Rather than assuming generated code works, the assistant can run the application, inspect the interface, identify problems, fix them and test again. The workflow becomes: idea, plan, worktree, code, run, visual verification, fix, test, commit and deploy. The real question is no longer whether AI can write code. It is whether we can structure projects and workflows well enough for AI to contribute safely and effectively as an engineering partner.

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