Most failed vibe coding projects don't fail because the AI was bad. They fail because the person building didn't know — with enough precision — what they were trying to create.
When your intent is vague, you get vague output. You accept it because you can't evaluate it. You build on top of it. And eventually you have something that sort of works but doesn't feel right — and you can't put your finger on why.
Clear intent is the difference between steering a project and drifting through one. It gives you the ability to evaluate every output, reject what's wrong, and know when you're done.
Every project has four dimensions of intent that you need to clarify before you start. Think of these as layers — each one makes the next more precise.
Describe the core action of your app in one sentence. Not the features — the fundamental job it does. Try this formula: [App name] helps [who] to [core action] so they can [outcome].
Name a specific type of person — not "everyone" or "small businesses." The more specific your user, the more clearly you can evaluate whether your app serves them. Describe their context, their frustration, and what they're doing before your app exists.
What problem is so real and persistent that someone would actually change their behaviour to use your tool? "Because it would be convenient" is not a why. The real why is a specific friction — a specific moment where the current way breaks down.
Describe your v1 success state in concrete terms. Not "it's polished" — but what specific journey can a user complete? If you can't describe success, you'll never know when to stop building. This also becomes your acceptance criteria for testing.
Before starting any vibe coding project, work through this worksheet. Take your time. The 20 minutes you spend here can save 20 hours of building the wrong thing.
Once you've completed the worksheet, your first AI prompt essentially writes itself. You don't need clever phrasing — you need to transmit your intent accurately. Here's how to structure it:
| The Pattern | What it looks like | How to fix it |
|---|---|---|
| Feature Soup | You have 15 features for v1. Each seems important. None is clearly the most important. | Force-rank them. Cross out everything below #4. Ship #1–3 and see if anyone uses them first. |
| The Invisible User | Your user is "anyone who wants to…" or "people who need to…" with no specific person in mind. | Name one actual person you know who has this problem. Build for them. Generalize later. |
| Solution First | You've described the app in detail but can't clearly articulate the problem it solves. | Write the before state (Field 03) first. If it's not compelling, question whether the solution is needed. |
| Moving Goalposts | Every session adds scope. What started as "a simple tool" now has 3 modules and an API. | Lock the v1 definition in writing. Every new idea goes on a v2 list. Nothing joins v1 once locked. |
| No Done State | You'll "know it when you see it." Sessions keep going because there's no finish line. | Write Field 06 (the done test) before you start. It doesn't matter if it changes — having one is what matters. |
Iteration isn't just "ask for more things." It's a disciplined loop: get output, evaluate it against your intent, identify the smallest thing to change, make that change, and test. Then repeat.
The size of your iterations matters enormously. Large, multi-part requests produce large, messy outputs that are hard to evaluate and harder to test. Small, focused requests produce outputs you can assess in 30 seconds.
The best vibe coders keep their iterations tiny — almost uncomfortably tiny. "Add input validation to this one field" beats "add validation everywhere" every time.
A powerful habit: every iteration should produce something testable. Not perfect — just testable. Ask yourself: "Can I run this and observe something?" If yes, run it. Don't wait for something to feel finished before you look at it working.
Every AI conversation starts from zero. The model doesn't know what you've built, what decisions you've made, what problems you've already solved, or why you made the choices you did. Every session, you are handing a brilliant but completely uninformed collaborator a task — and the quality of the handoff determines everything.
The context gap is the distance between what you know and what you've told the AI. Closing it isn't about writing longer prompts — it's about writing precise, targeted prompts that give the AI what it actually needs for this specific task.
The goal isn't to check everything — that would defeat the purpose of vibe coding. The goal is to verify strategically: to focus your attention where AI errors are most costly and most likely.
For each piece of AI-generated code you accept into your codebase, run through this checklist. It takes under two minutes and catches the majority of silent failures:
Architecture is not a single decision — it's the accumulation of structural decisions that determine how easy or hard it is to change things later. The wrong architecture doesn't break your app today. It makes every future change progressively more painful.
AI makes architectural decisions when you let it — and it makes them based on pattern-matching from its training, not based on your specific constraints, future plans, or tolerance for complexity. It will choose what's common, not what's right for you.
The practical rule: let AI build the rooms, but you draw the floor plan.
Taste is not aesthetics, though it includes aesthetics. It's the capacity to evaluate output against a standard that you hold but might struggle to articulate. It's the nagging feeling that something is off — and the discipline to not ship it anyway.
In vibe coding, taste shows up as: knowing when a component is doing too much. Feeling that an interaction is one step too complicated. Noticing that the code has the right behaviour but the wrong structure. Sensing that a feature, while requested, doesn't actually serve the user you described in your worksheet.
AI will produce technically correct output. Taste is the filter between "technically correct" and "actually good."