How to Vibe-Code Without Making a Mess

I've been building with AI for a couple of years now, and I've learned one thing: "prompt and pray" doesn't work. You end up with bloated components, inconsistent naming, and a codebase that's impossible to maintain.

But when you treat the AI as a junior developer—giving it clear specs, checking its work, and holding it to high standards—you can build incredibly fast without sacrificing quality. This is the essence of vibe-coding.

This article breaks down the exact process I used to build UpvoteVibe. It's not magic; it's a disciplined, iterative approach that you can use for your own projects.

The Setup: Your Guardrails for Quality

Before you write a single prompt, you need a system to keep the AI on track. This is non-negotiable. My system has two parts: a set of core principles and a dual-documentation structure.

The 5 Principles of Clean Vibe-Coding

This is the cycle I run for every single feature. It's a simple loop that prioritizes clarity and verification.

1. Hypothesis First: What am I building, and why? What's my technical theory?
2. Focused Spec: A tight brief (~1500 words) covering the capability, interface, and success criteria.
3. Minimal Implementation: Ask the AI for the simplest possible version that works.
4. Verify and Refine: Test the output. Does it meet the spec? Is it clean?
5. Document Learnings: What worked? What didn't? Capture it for next time.

The Dual-Documentation System

This is the secret weapon. I separate what the system does from how I built it.

1. Feature Specifications (The "What")

This is the AI's context. It's a permanent, evolving record of the system's capabilities.

specs/features/
├── profile-menu-and-my-projects/
│   ├── capability.md          # What this enables for users
│   ├── interface.md           # Component APIs and data flow
│   ├── dependencies.md        # What it needs from other features
│   └── tests.md               # Success criteria and test cases

2. Development History (The "How")

This is the project's logbook. It's an append-only record of the implementation journey, warts and all.

specs/development-history/
├── core-v1-auth-and-projects/
│   ├── hypothesis.md          # My original theory
│   ├── plan.md                # Task breakdown and progress
│   ├── approach.md            # Technical decisions made
│   └── results.md             # What was actually delivered

This separation is critical. The feature specs stay clean and focused, which is perfect for the AI. The development history captures the messy reality of the build process, providing invaluable context for the future.

The Play-by-Play: Building Project Submission

Here's how I applied this system to build the project submission feature on UpvoteVibe.

Step 1: Hypothesis (5 mins)

"I believe I can create a simple, intuitive form for users to submit their projects. It needs to capture key metadata, validate inputs, and provide clear feedback on success or failure."

Step 2: Focused Spec (45 mins)

I wrote a spec that defined the Project data type, outlined the validation rules (using Zod), and detailed the success and error states of the form.

Step 3: AI Collaboration (15 mins)

My prompt to Claude was specific and directive:

"Implement the project submission feature based on the attached spec. Use Next.js App Router, Server Actions, and Zod for validation. The form should be a client component that calls a server action. Keep the implementation as minimal as possible."

Step 4: Verify and Refine (30 mins)

The AI's first pass was about 80% there. It handled the happy path perfectly. I then prompted it for refinements:

"This is a good start. Now, add error handling for network failures. Display a toast notification to the user if the submission fails. Also, ensure the form fields are disabled while the submission is in progress."

Step 5: Document Learnings (10 mins)

I recorded my notes in the development history: "Server Actions are a great fit for this kind of form. The initial validation logic from the AI was a bit too complex; simplifying it made the code easier to read and maintain."

The Tech Stack: My Opinionated Choices

I didn't ask the AI to choose my stack. As the senior engineer on the project, that's my job. Here's what I chose and why:

• Next.js 15, React 19, TypeScript: The modern standard for building robust, SEO-friendly web apps.
• Firebase (Auth & Firestore): Scalable, easy to use, and cost-effective for indie projects.
• Tailwind CSS v4: Utility-first CSS is the fastest way to build custom designs.
• Server Actions: The default for mutations in the App Router. Simplifies the code and improves security.
• Vitest & Storybook: For a fast, reliable testing and component development workflow.

You could use a different stack, of course. The key is to make a deliberate choice and then use the AI to implement within that framework, not to create the framework for you.

This approach—structured specs, iterative refinement, and clear ownership of architectural decisions—is what allowed me to build UpvoteVibe quickly and cleanly. It's a repeatable process that turns the AI into a powerful collaborator, not a source of chaos.

Ready to put it into practice? The next play shows you how to define a rock-solid product vision—the essential first step for any successful project.

Next: Nail the Product Vision