Vibe Coding Guide: 5 Pro Tips to Get the Most Out of AI for Building Apps

AI coding tools are changing the way we build software, making it possible for developers—beginners and pros alike—to turn ideas into working applications faster than ever. But if you’ve spent any time using AI for coding, you’ve probably realized that it’s not as simple as typing “build me an app” and getting perfect results.

Vibe coding is the art of using AI to write code efficiently while keeping the development process smooth and frustration-free. The goal isn’t just to generate code but to work with AI effectively, avoiding common pitfalls that slow you down.

This guide breaks down five pro tips for making AI work for you, not against you. Whether you’re building a web app, integrating APIs, or debugging a stubborn issue, these techniques will help you maximize AI’s potential and ship products faster.

1. Pick a Popular Tech Stack

The biggest mistake people make when using AI for coding is choosing the wrong tech stack. AI models, even the best ones like Claude Sonnet, GPT-4, or DeepSeek, learn from publicly available code. That means the more popular a framework or language is, the more AI understands it.

If you pick a tech stack that AI is less familiar with, you’re setting yourself up for unnecessary pain. AI won’t be able to help as much, it will produce more incorrect code, and debugging will take longer.

For most web apps, Next.js + Supabase is the best choice. It’s well-documented, has a massive community, and, more importantly, AI has seen a ton of Next.js code during training. If your project needs a backend that Supabase can’t handle, adding Python is a solid choice.

Other stacks might be cool—Svelte, Vue, SolidJS—but cool doesn’t mean practical when working with AI. AI struggles with niche frameworks, and that means you’ll be fighting bugs alone.

What Happens If You Ignore This?

• You’ll spend more time debugging than coding.
• AI will suggest broken solutions because it doesn’t “know” your stack well.
• You’ll hit roadblocks that AI simply can’t help with.

What Happens When You Follow This?

• AI generates higher-quality code that actually works.
• Debugging is easier because AI understands the framework’s common issues.
• You build and ship faster instead of wasting hours on fixes.

If you’re thinking about using AI for game development, stop right there. Unless you’re diving into Unity or Unreal Engine, AI isn’t going to help much. Those little Three.js or web-based “vibe games” people make? No one plays them, and AI struggles with game logic beyond basic demos.

2. Use a Product Requirement Document (PRD)

A huge reason AI-generated code fails is because the instructions given are too vague. AI isn’t a mind reader—it needs clear, structured guidance.

This is where a Product Requirement Document (PRD) comes in. It forces you to think through your app’s features, break them down into small, testable steps, and give AI proper direction.

A Product Requirement Document (PRD) is one of the most underrated tools for successful AI-assisted coding. Many developers assume AI can figure things out on its own, but the reality is that AI thrives on clarity and structure. A PRD forces you to define exactly what you want to build, breaking down vague ideas into actionable, testable components.

Without a well-structured PRD, you’ll constantly find yourself in AI ping-pong mode—where you keep asking AI to fix broken or incomplete implementations, only to get new errors in return. AI models like Claude Sonnet or GPT-4 Turbo can generate functional code, but if your instructions are vague, the output will be equally vague, full of incorrect assumptions, missing logic, and inconsistencies.

How to Use a PRD with AI

1. Write out exactly what your app needs to do in plain English.
2. Break it into smaller, individual tasks.
3. Feed these tasks to AI one at a time.

Instead of dumping the whole PRD into AI at once, take it step by step.

• Start with:

  “Here is the project plan. Start with Step 1.1: Add user authentication.”

• Once authentication is working, move to:

  “Now implement Step 2: Add user profiles.”

What Happens If You Skip This?

• AI will generate vague or incomplete code that doesn’t actually work.
• Debugging becomes a nightmare because everything is tangled together.
• You’ll waste hours fixing issues that could have been prevented.

What Happens When You Use a PRD?

• AI produces cleaner, more structured code that works as expected.
• Fixing bugs becomes easier since everything is built step by step.
• Your project moves forward faster, with fewer roadblocks.

3. Use Version Control

If you’re relying on AI to code, at some point, AI will destroy your codebase. It’s not an “if,” it’s a “when.”

One day, you’ll ask AI to refactor something simple, and instead, it will rewrite or delete working code. If you don’t have proper version control, you’ll be stuck manually reconstructing your project from memory.

Using Git + GitHub/GitLab is non-negotiable. It lets you track every change, revert to older versions, and save yourself from AI-related disasters.

What Happens If You Don’t Use Version Control?

• AI will overwrite working code, and you won’t be able to restore it.
• Debugging gets harder because you don’t know what changed.
• If AI makes a mistake, you’re starting over from scratch.

What Happens When You Use It?

• You can easily undo AI’s mistakes without stress.
• You track progress and changes, making debugging faster.
• Your project stays organized and secure.

4. Provide References of Docs & Code Samples

AI is great, but it isn’t always right, especially when dealing with APIs or third-party integrations. If you blindly trust AI to read documentation correctly, you’ll waste hours debugging broken implementations.

The Better Approach:

1. Find a working code snippet (from docs, Stack Overflow, etc.).
2. Test it manually to confirm it works.
3. Save it in a file (e.g., jira-test.md).
4. Use it as a reference for AI when building features.

Instead of saying:

“Add Jira integration.”

Give AI a working reference and say:

“Implement Step 4.1: Jira integration. Reference jira-test.md.”

5. Start New Chats with Bigger Models When Stuck

If you keep asking the same AI session to fix a bug and it’s failing repeatedly, stop. Start a new chat.

When AI chat history gets too long, the model starts to lose track of details and gives worse answers. This is where better models like Claude Sonnet, DeepSeek, or GPT-4 Turbo can help.

When switching to a new chat, tell AI:

• What’s not working
• What you expect to happen
• What you’ve already tried
• Console logs, errors, and screenshots

Final Thoughts on Mastering Vibe Coding

Vibe coding is about working smarter, not harder. AI can be an incredible tool, but only if you know how to use it properly. Follow these tips, and you’ll avoid common pitfalls, code faster, and build real, working applications.