Vibe Coding Guide: How to Build with AI Like a Pro

I’ve been working with AI-assisted coding for a while, and I’ve seen the rise of what the internet calls “vibe coding.”

If you’re building with LLMs (large language models), you’ve probably already done it, even if you didn’t have a name for it.

The first time I heard the term was from Andrej Karpathy, who described it as “programming in English.”

That definition stuck with me because it’s exactly what this is: fast iteration, natural language prompts, and feedback loops that prioritize speed over structure.

This guide is everything I’ve learned about how to do vibe coding well.

It’s not just about using AI to spit out code. It’s about working differently: letting go of the need to control every line, and instead focusing on the feedback you get from running the app.

If you’ve been burned by flaky codegen or bloated AI outputs, this guide will show you how to take the good parts of vibe coding while keeping your builds maintainable.

Let’s get into it.

What Is Vibe Coding?

Vibe coding is when you use natural language to tell an AI model what you want, get back code, and test it immediately instead of analyzing every line.

You don’t write big specs or plan out detailed architecture. You just run the thing, look at what breaks, and fix it using prompts.

This style works because LLMs are very good at filling in the blanks, and often faster at scaffolding than humans are.

The shift here is that the developer acts more like a product manager: you describe what you want and course-correct as you go.

Some developers argue that if you’re thoroughly reviewing the generated code and writing proper tests, you’re not really vibe coding, you’re just doing AI-assisted development.

That’s a fair distinction, and honestly, I think it’s a spectrum. For me, vibe coding is about fast feedback loops, not skipping best practices entirely.

Core Traits of Vibe Coding

Natural language first
Minimal upfront planning
Fast iteration by running, not reading
Debugging through prompts
Less about code quality, more about outcome

When It Feels Right

You’re building a prototype in a day
You don’t care about scale yet
You’re trying to learn a new API or library
You’re hacking together internal tools or scripts

When It Goes Wrong

Production apps with sensitive logic
Compliance-heavy environments
Apps that need long-term maintainability
Anything involving money, security, or personal data

If you’ve ever built a tool in a weekend that “just worked,” then vibe coding is for you. Just don’t ship it to thousands of users without cleaning it up.

The Vibe Coding Workflow That Actually Works

You can absolutely vibe your way into a broken mess. I’ve done it. That’s why I follow a five-step workflow now that keeps things clean without losing the speed.

1. Micro-Spec First

Start with a tiny spec. Give your AI model enough context so it doesn’t go off the rails.

What to include in your micro-spec:

One-sentence goal: “Build a tool that takes a CSV and returns a cleaned JSON.”
Users and flows: “A data analyst uploads a CSV and downloads the cleaned file.”
Success criteria: “Returns valid JSON with no empty fields.”
Constraints: “Use Python, no external libraries outside Pandas.”
Definition of done: “CLI tool, can run with one command, includes one test file.”

You don’t need a Gantt chart. You just need clarity.

2. Ask for a Plan, Not Code

Before asking for any code, prompt the model to lay out a plan. Otherwise, you’ll end up with “surprise architecture” that’s impossible to debug later.

What to ask for:

File structure
Data models or schema
Main components
Dependencies
Order of implementation
Potential risks or unknowns

Example prompt: “Before writing any code, give me a file tree, data models, dependencies, and a list of implementation steps.”

This makes it easier to control scope and avoid repetition.

3. Generate in Small Slices

Don’t ask for 500 lines of code in one go. Break it into PR-sized chunks. This improves accuracy and makes it easier to debug.

Slice ideas:

Set up the project scaffold
Add just the login screen
Build one API endpoint
Add tests for a single function
Refactor one feature

Smaller prompts get better results, and they reduce hallucinations.

4. Run, Test, Fix

The magic of vibe coding is in the feedback loop. Don’t just look at the code. Run it. Click around. Break it on purpose.

Here’s my bare-minimum loop:

Run the app locally
Walk the happy path (does it work?)
Try to break something (edge cases, bad input)
Copy any errors into your next prompt and ask the AI to fix them

The more you rely on this loop, the more reliable your code becomes—even if you didn’t fully understand how the first version worked.

5. Add Guardrails Early

Once you’ve got a scaffold that runs, it’s time to protect it.

What I always include:

Git for version control (yes, from day one)
Code formatter (like Prettier or Black)
Linter (ESLint, Flake8, etc.)
Type checker (TypeScript, MyPy)
Test harness (Jest, Pytest)
Env management (.env files and secret handling)

This setup costs about 15 minutes but saves you hours later.

Prompt Patterns That Make Life Easier

I’ve tested hundreds of prompts. The most consistent ones follow clear structures and avoid ambiguity. Here are four prompt styles I use regularly.

A. The Build Prompt

Used when creating something new.

Prompt components:

What to build
Constraints (language, framework)
Return format (changed files only)
Setup instructions
Risks and assumptions

Example: “Build a FastAPI route that accepts a JSON payload and returns a PDF. Use no external PDF libraries. Return only changed files with paths. Include how to run and test in 5 lines. List any risks or assumptions.”

B. The Change Request

Used to update existing behavior.

Structure:

Current behavior
Desired behavior
Acceptance criteria
Constraints
Edge cases

Example: “Currently, the form submits data to /api/upload. I want it to instead save the file to an S3 bucket. Acceptance: file appears in the bucket, and the UI shows a success message. Keep all other behavior unchanged.”

C. The Bug Report

Used to squash bugs fast.

What to include:

Steps to reproduce
Expected vs actual outcome
Full error message
Environment info

Follow-up prompt: “Give 3 likely root causes. Pick the most likely and write the smallest fix. Add a regression test.”

Reddit threads have shown this is where vibe coding often fails. Debugging by prompt is harder than building. Stay patient.

D. The Quality Pass

When things work, it’s time to clean up.

Checklist:

Remove unused code and dependencies
Simplify logic flows
Add tests for critical paths
Perform a basic security review
Write documentation for setup and structure

Do this in small rounds so you don’t overwhelm the AI.

When to Avoid Vibe Coding

Just because something can be built by vibe doesn’t mean it should. Here’s when I slow down and go back to traditional workflows.

Scenarios to Avoid

Scenario	Why It’s a Problem
Payment handling	Small bugs can cost real money
Authentication	One flaw opens the door
Permissions logic	Easily breaks with AI mistakes
Compliance apps	Legal exposure is too high
Apps with public APIs	Users depend on stability
Medical/health tools	Liability risk is huge

Vibe coding is great for learning and prototyping. It’s not great for things that can fail catastrophically.

If It Works, How Do You Take It to Production?

Let’s say you vibe out an app in two days, and it actually works. Now what? You don’t just ship it. You “graduate” it.

Graduation Plan

Freeze development for one week
Let the dust settle. Don’t make new changes.
Write a one-page architecture doc
Explain what talks to what. Where’s the data? How does auth work?
Decide what must be human-readable
Refactor confusing functions. Add comments. Improve naming.
Add logs, monitoring, and deployment steps
Use tools like LogRocket, Sentry, or Datadog. Set up GitHub Actions or Netlify.
Rewrite fragile parts
If a feature is hard to test, rewrite it manually with better logic.

This doesn’t mean throwing out your prototype. It means turning it into real software.

The Open Source Catch

A big part of vibe coding depends on open-source libraries. If the community that maintains those libraries burns out, we all lose.

There’s already research showing that heavy AI code generation may lead to less engagement with open-source projects, because fewer devs are actually reading the source code.

If a package saves you five hours, consider:

Submitting bug reports
Opening a PR
Writing docs
Sponsoring the maintainer

This isn’t about ethics. It’s about keeping your tools alive.

How to Try Vibe Coding Today

If you’re new to this, here’s my starter recipe.

Simple Vibe Coding Starter Plan

Pick a tiny app
Something with one screen and one main action.
Write a 10-line micro-spec
Include goals, flow, stack, and output.
Ask for a plan, not code
Get file tree, models, dependencies.
Generate the scaffold
Prompt for a starter project only.
Build one feature end to end
Don’t move on until it works.
Add a basic test
Even one test makes a big difference.
Stop while it’s still fun
Don’t overdo it. You’ll learn faster this way.

This will give you a working proof of concept in under an hour. After that, iterate at your own pace.

Final Thoughts

Vibe coding isn’t hype. It’s a shift in how we build. It doesn’t replace good engineering, but it changes what “good” looks like during the first few hours or days of a project.

Fast feedback, tight loops, and simple prompts are your new best friends.

Just remember: if the code’s going to live for more than a week, it deserves better than vibes. Use the AI to move fast, then use your brain to make it strong.