Ming Zou

TL;DR: Prompting AI directly from a rough idea produces rough results. Running a spec step first — where you clarify scope, constraints, and intent before any code gets written — dramatically reduces revision cycles and gets you closer to what you actually wanted on the first try.

Here is a pattern I have seen repeat itself more times than I would like to admit. You have a feature in your head. You open an AI coding tool, describe it in a sentence or two, and watch the code appear. It looks reasonable. You try it. Something is off — not broken exactly, but not quite right either. You iterate. You re-prompt. You adjust. Forty minutes later you have something that works, but the path to get there was messier than it needed to be.

That is vibe coding in its natural state. Fast to start, fuzzy to finish.

I ran into this enough times building a side project that I started looking for a better on-ramp. What I found was GSD, and specifically its spec-driven workflow. The difference in output quality was noticeable enough that it changed how I approach AI-assisted development.

The Problem with Vibing Directly

When you prompt an AI with a rough idea, you are outsourcing two things at once: the what and the how. The AI has to infer your intent from an incomplete description and then implement it. That is a lot of guesswork packed into a single step.

The output reflects that. You get code that is technically coherent but misaligned in subtle ways — the wrong abstraction, a scope that is slightly too wide or too narrow, an interaction model that is not quite what you pictured. None of these are bugs the AI can fix, because from its perspective it did exactly what you asked. The problem is that what you asked was underspecified.

This is what engineers mean when they talk about AI slop. It is not that the code is bad. It is that the code is a confident answer to the wrong question.

What a Spec Step Actually Does

Before writing any code, GSD runs you through a structured interview. It breaks down your feature into implementation categories — things like scope, user interactions, data requirements, edge cases — and asks targeted questions in each one. You are not writing a formal spec document. You are just being asked to think clearly about what you are building before the AI starts building it.

The questions surface assumptions you did not know you were making. Scope that felt obvious turns out to be ambiguous. Requirements you thought were simple have edge cases worth considering upfront. By the time the interview is done, you have a much clearer picture of what you actually want — and so does the AI.

That clarity is what changes the output. When the AI generates code from a well-scoped spec rather than a rough prompt, the result is closer to what you intended on the first pass. Fewer revision cycles. Less course-correcting. Less slop.

What This Looks Like in Practice

Working on my side project, the difference showed up quickly. Features I built spec-first required one or two rounds of refinement. Features I had prototyped earlier by vibing directly often needed five or six — sometimes more — before they landed where I wanted them.

The spec step is not magic. It works because it forces a conversation about intent before implementation begins. You end up writing a better prompt, essentially, except the process of arriving at that prompt also sharpens your own thinking. You know what you are building more clearly than you did before you started.

That has a compounding effect. When you are clear on scope, the AI does not over-engineer. When you have thought through edge cases, they get handled the first time instead of discovered later. When your requirements are explicit, the implementation reflects them.

The Honest Tradeoff

This approach has a real cost: time upfront. For a substantial feature, that is worth it. For small, well-understood changes — updating a component's styling, renaming a variable, fixing a layout — it is overkill. Running a spec interview before changing the color of a button is not good engineering judgment, it is ceremony for its own sake.

The skill is knowing when the spec step earns its cost. A good rule of thumb: if you can describe the full implementation in one sentence with no ambiguity, skip it. If you find yourself hesitating over details when you try to describe it, that hesitation is exactly what the spec step is designed to resolve.

A Shift in How to Think About AI Coding

The standard mental model for AI-assisted development is: describe what you want, get code, iterate until it is right. That model treats iteration as the primary mechanism for accuracy. You converge on the right result by bouncing back and forth with the AI until the output matches your intent.

Spec-driven development inverts this. Accuracy comes from clarity upfront, not from iteration after the fact. The goal is to enter the code generation step with an intent that is precise enough that the first output is close to correct — not perfect, but close.

That shift sounds simple, but it meaningfully changes the experience. Less time fixing. More time building. And a lot less of that particular frustration where you know the code is wrong but struggle to articulate exactly why.

If you want to try this workflow, GSD is where I would start. The spec-driven approach is built into how it structures development, and the interview process is what makes the difference. It is not the only way to get here, but it is the most systematic one I have found.