How to Vibe Code: A Complete Guide

Vibe coding is a different approach to building software. Instead of learning syntax and debugging line by line, you describe what you want in plain language and let AI or no-code tools generate the code for you.

Andrej Karpathy, former Tesla AI director, popularized the term in early 2025 when he tweeted about "just vibing" with AI to write code. Since then, founders, product managers, and non-technical builders have used this method to ship MVPs without traditional programming. OpenAI CEO Sam Altman calls this the era of "software on demand."

Let’s explore how vibe coding works, which tools and AI models fit different needs, and when it’s time to move toward traditional development.

What Is Vibe Coding?

Vibe coding emerged from the AI-assisted development wave of 2024-2025. Karpathy's tweet crystallized what many were already doing: using conversational AI to generate functional code by describing intent rather than writing logic.

The term is now a trending buzzword in 2025. It's about letting AI drive your development process, throwing away rigid roadmaps, and following the creative sparks that emerge in real time. You start with an idea, give the AI direction, and see where the conversation leads.

How It Differs from Traditional Coding

Traditional coding requires you to learn a programming language, understand data structures, and translate business logic into precise syntax. You think in terms of functions, loops, and variables.

Vibe coding inverts this. You think in terms of outcomes. "Build me a landing page with an email signup form that saves to a database" becomes a prompt rather than hundreds of lines of HTML, CSS, JavaScript, and backend code.

The catch is that you still have to keep your hands on the wheel at some point to bring the project where you actually want it to go. In vibe coding, the dynamic between you and the AI isn't about replacing you; it's about shifting roles.

Why Vibe Coding Matters in 2025

Vibe coding became practical because a few key developments changed how people build software. GitHub Copilot can reliably autocomplete code. ChatGPT and Claude let you generate functions, scripts, and small applications from plain language. No-code platforms like Bubble and Webflow now handle more complex logic.

OpenAI used GPT-5 to create functional web apps from simple prompts. In one example, it generated two versions of a language-learning app with quizzes, flash cards, and progress tracking within minutes.

The result is that prototyping software no longer requires a full team. A single person can test and iterate an idea over a weekend.

Benefits for Non-Developers and Rapid Prototyping

For founders testing ideas, vibe coding changes the economics of validation. Instead of spending $20,000-50,000 on an MVP, you can build a working prototype for the cost of a few subscriptions.

Product managers can create functional demos rather than static mockups. Entrepreneurs can test market demand before hiring engineers. Side projects that would have stayed in notebooks can become real products.

Parker Ortolani put it: "I've spent more than a decade designing static concepts, but now I can make those concepts actually work. It changes everything for someone like me."

How to Start with Vibe Coding

Choosing the Right Platform

Different tools suit different project types:

For web applications: Bubble, Webflow, or Framer handle frontend and basic backend logic. They work well for marketplaces, dashboards, and content sites.

For mobile apps: Adalo and Glide create native-feeling apps from spreadsheets or databases. Good for internal tools and simple consumer apps.

For AI-assisted coding: Cursor, Windsurf, Claude Code, or direct prompting with ChatGPT/Claude generate actual code you can deploy anywhere.

For rapid prototyping: v0 by Vercel generates React components from descriptions. Bolt and Lovable create full-stack applications from prompts.

Start with one tool that matches your project type. Switching tools mid-project adds friction.

Describing Your Idea in Plain Language

Good prompts produce better results. Be specific about:

• What the user sees (interface elements, layout).

• What happens when users interact (button clicks, form submissions).

• Where data goes (storage, external services).

• Edge cases (what if the form is empty, what if the user isn't logged in).

The biggest mistake people make is being incredibly vague. The AI has no context. What does your business do? What's the color scheme? What sections do you need?

Bad prompt: "Build me a todo app."

Better prompt: "Build a todo app where users can add tasks with a title and due date, mark tasks complete, filter by status (all/active/completed), and data persists in local storage."

The more context you provide, the closer the output matches your intent.

Iterating and Refining Your Project

Vibe coding is iterative by nature. Your first output will need refinement. Test each version, identify gaps, and adjust your prompts or manual edits.

Keep your iterations small. Instead of describing an entire application at once, build feature by feature. This makes debugging easier and keeps the AI focused.

What Language or Tools Are Best for Vibe Coding?

Text-to-App Platforms and No-Code Tools

For non-coders, pure no-code platforms offer the smoothest experience:

• Bubble: Full-stack web apps with visual programming

• Webflow: Marketing sites and content-heavy applications

• Glide: Mobile apps from Google Sheets

• Airtable + Softr: Database-driven applications

For those comfortable reviewing code:

• Cursor: AI-first code editor that writes and edits code from prompts

• Windsurf: Similar to Cursor with strong agentic capabilities

• Claude Code: Command-line tool for agentic coding with Claude

• Replit: Browser-based IDE with AI assistance

When to Use Actual Programming Languages

Vibe coding works best for prototypes and MVPs. As applications grow, you'll likely need traditional code for:

• Custom integrations with external APIs

• Performance optimization

• Complex business logic that no-code tools handle poorly

• Compliance requirements that need auditable code

• Features that push platform limits

Many successful projects start with vibe coding, validate the idea, then rebuild critical parts with traditional development.

Can You Use ChatGPT or Claude for Vibe Coding?

Yes, and both have evolved significantly for this use case. The latest models from OpenAI and Anthropic represent major leaps in coding capability.

GPT-5 for Vibe Coding

GPT-5 is OpenAI's best model for coding and agentic tasks. It scores 74.9% on SWE-bench Verified and 88% on Aider polyglot benchmarks. OpenAI trained it specifically as a coding collaborator.

It designs beautiful user interfaces on its own without needing guidance and is infinitely more reliable than previous models. Where older models like GPT-4o would frequently introduce bugs that broke apps, GPT-5 has caused zero build errors in Xcode for some users.

Cursor calls GPT-5 "the smartest model they've used" and "remarkably intelligent, easy to steer." Windsurf reports it has half the tool calling error rate over other frontier models. Vercel says "it's the best frontend AI model, hitting top performance across both aesthetic sense and code quality."

When vibe coding with ChatGPT, OpenAI recommends opening multiple chats with GPT-5, pasting the same prompt into all of them, then continuing with the chat that created your favorite prototype. Each chat produces something different.

Claude for Vibe Coding

Claude Sonnet 4.5 is Anthropic's best coding model, claiming state-of-the-art performance on SWE-bench Verified. It excels at sustained, complex coding tasks.

Anthropic AI researcher David Hershey says he's seen Claude Sonnet 4.5 code autonomously for up to 30 hours during early trials. In that time, the model not only built an application but also stood up database services, purchased domain names, and performed a SOC 2 audit to make sure the product was secure.

Dan Shipper conducted head-to-head tests, noting that Claude Sonnet 4.5 processed a large pull request in minutes, handled multi-file reasoning without wandering, and stayed concise. Separately, Kieran Klaassen used Sonnet 4.5 in Claude Code to vibe code an entire iOS app by feeding it the existing codebase and a set of instructions.

Which One Should You Use?

Both work well. Some differences:

• GPT-5 is cheaper at $1.25 per million input tokens versus Claude Sonnet 4.5 at $3 per million.

• Claude Sonnet 4.5 is noticeably faster than GPT-5 Codex for day-to-day tasks.

• GPT-5 excels at frontend work and one-shot app generation in ChatGPT's Canvas.

• Claude works better for longer coding sessions and multi-file reasoning.

Many developers use both, switching based on the task.

Example Prompt Workflow

Here's a typical flow for building a feature:

Prompt 1: "I need a React component that displays a list of products with name, price, and image. Users should be able to filter by category using a dropdown."

AI generates: Initial component code with basic filtering.

Prompt 2: "Add a search bar that filters by product name in real-time as the user types."

AI updates: Adds search functionality to existing code.

Prompt 3: "The filtering is slow with 500+ products. How can I optimize this?"

AI suggests: Debouncing, memoization, or virtualized lists with implementation code.

Each prompt builds on the previous result, refining toward your goal.

How to Build an App Using Vibe Coding

Step 1: Write out the user flow before opening any tool. Describe what happens from app open to task complete. "User opens app → sees expense list → taps add → enters amount and category → saves → sees updated total." If you can't write this in plain sentences, the AI will guess, and it will guess wrong.

Step 2: Start a fresh chat for each major component. AI context degrades over long conversations. Build your authentication in one chat, your main feature in another, your settings page in a third. Combine the code yourself. This produces cleaner output than one sprawling conversation.

Step 3: Prompt for structure first, then details. Ask for the skeleton: "Create a React component with a form that has fields for title, amount, and category. No styling yet." Once that works, follow up with: "Add Tailwind styling to make it look like a modern card interface." Layering keeps each step debuggable.

Step 4: When the AI generates broken code, paste the exact error. Don't describe the problem. Copy the full error message and paste it. Say "I get this error when I run the code" and nothing else. The AI fixes errors faster when it sees the actual output rather than your interpretation.

Step 5: Keep a prompt log. Save every prompt that produced good results. When you start a new project, you'll have tested prompts to reuse. This compounds over time and becomes your personal toolkit.

Step 6: Test with someone who doesn't know your idea. Watch them use it without explaining. Where they pause, tap wrong, or look confused reveals what needs work. This costs nothing and surfaces problems you'll never find yourself.

Step 7: Deploy before it feels ready. Vercel deploys a Next.js app in under two minutes. Netlify handles static sites instantly. A live URL forces you to confront real issues: slow loads, broken mobile layouts, missing error states. Local development hides these.

Step 8: Version control from day one. Even if you don't know Git well, commit after every working change. When the AI breaks something three prompts later, you can roll back. Without version control, you'll rebuild from memory.

Common Pitfalls and How to Avoid Them

Over-prompting: Asking for too much at once produces messy results. Break complex features into smaller requests.

Skipping testing: Generated code can have subtle bugs. Test each feature before building on top of it.

Ignoring performance: Vibe-coded apps can be slow. Monitor load times and optimize before they become problems.

No version control: Even with no-code tools, track changes. Bubble has version history; for generated code, use Git.

How to Vibe Code Safely and Responsibly

Security Best Practices

Vibe-coded applications handle real user data. Take security seriously:

• Never expose API keys in frontend code.

• Use authentication for any user-specific data.

• Validate inputs on the server, not just the client.

• Review generated code for obvious vulnerabilities (SQL injection, XSS).

• Use HTTPS everywhere.

No-code platforms handle some security automatically, but you're still responsible for how you configure them.

Data Privacy and Maintenance Considerations

Consider long-term implications:

• Where does user data live? Can you export it if you switch platforms?

• Who has access to your database? Review platform security practices.

• How will you maintain this application? Document your prompts and decisions.

• What happens if the platform shuts down? Have a migration plan.

Where Vibe Coding Falls Short

Vibe coding has real constraints:

Customization ceilings: No-code platforms limit what you can build. When you hit those limits, workarounds get ugly.

Performance: Generated code often isn't optimized. High-traffic applications may struggle.

Vendor lock-in: Bubble applications can't be exported as code. Switching platforms means rebuilding.

Debugging complexity: When something breaks in generated code you don't understand, fixing it is harder than if you wrote it yourself.

Scalability: Architecture decisions that don't matter at 100 users become critical at 10,000.

When to Switch to Traditional Development

When Your MVP Needs to Scale

You should consider moving beyond vibe coding when:

• User growth is straining platform limits.

• You're spending more time on workarounds than features.

• Performance issues affect user experience.

• Security or compliance requirements exceed what your tools support.

• You need integrations that don't exist as plugins.

The Role of Software Craftsmanship in Scaling

Prototype code and production code serve different purposes. Vibe coding optimizes for speed to market. Production systems need maintainability, test coverage, security hardening, and architecture that handles growth.

This isn't a criticism of vibe coding. It's recognizing that tools fit different stages. A prototype validates your idea. Production code sustains your business.

How Development Partners Can Bridge the Gap

Many teams hire experienced developers to transition from prototype to production.

The right partner can:

• Audit vibe-coded prototypes for security and performance issues.

• Rebuild critical components while preserving what works.

• Design architecture that scales with your growth.

• Apply AI-assisted development to maintain velocity.

The goal isn't starting over. It's building on your validated prototype with engineering practices that support long-term growth.

Your Next Step

Pick one small project, not your startup idea or the app you've been planning for months, but something you can build in a weekend: a personal dashboard, a simple tool you'd actually use, a landing page with a working form.

Open ChatGPT, Claude, or Cursor, describe what you want, see what comes out, fix what breaks, and deploy it somewhere live. That first deployed project teaches you more about vibe coding than any guide. You'll learn how to prompt, how to debug AI output, and where the limits are. Start small, ship fast, and build from there.

You can also connect with our developers to review your MVP and plan a scalable architecture, ensuring your prototype evolves smoothly into a production-ready product.

Frequently Asked Questions

How do I start with vibe coding?

Pick a simple project and one tool. If you want to avoid code entirely, start with Bubble or Glide. If you're comfortable reading code even if you can't write it, use Cursor or Claude Code. Don't combine multiple tools on your first project. Build the smallest useful version of your idea, deploy it, then iterate based on what you learn.

Can I vibe code with ChatGPT or Claude?

Yes, and both have models built specifically for this. GPT-5 scores 74.9% on SWE-bench Verified and excels at frontend work and one-shot app generation. Claude Sonnet 4.5 handles longer coding sessions and multi-file reasoning well, with users reporting 30+ hours of autonomous coding on complex projects. Many developers use both, switching based on the task. GPT-5 is cheaper ($1.25 per million input tokens vs $3 for Claude), while Claude tends to be faster for iterative work.

How do I vibe code safely?

Never put API keys in frontend code. Use environment variables and keep secrets server-side. Add authentication before storing any user data. Validate inputs on the server, not just the client, since client-side validation can be bypassed. Review generated code for SQL injection and XSS vulnerabilities before deploying. Use HTTPS everywhere. If you're handling payments or sensitive data, get a security review before launch.

What language is best for vibe coding?

JavaScript/TypeScript and Python get the best results from AI models because the training data includes more examples in these languages. TypeScript works well for web apps since you can use React, Next.js, and deploy easily to Vercel. Python works well for backends, data processing, and scripts. If you're using no-code platforms like Bubble or Glide, you don't need to choose a language at all.

Can I build a real app with vibe coding?

Yes, but understand the considerations. Vibe coding gets you to a working prototype fast. Many production apps started this way. The limits show up when you hit 10,000+ users, need complex integrations, or face compliance requirements. At that point, you'll likely need traditional development to refactor for performance, security, and maintainability. Vibe coding validates the idea; traditional development scales it.