Something interesting is happening in how people build software. Devs are working alongside AI tools to plan, test, and write code in new ways, and non-devs are turning ideas into real projects without the need for deep technical knowledge. The gap between an idea and what it takes to build it is getting smaller. That's where vibe coding comes in.
The idea behind it is simple, but getting it right takes practice. You still need solid fundamentals and need to learn how to communicate with these AI tools to get the most out of them.
In this vibe coding guide, we'll look at what vibe coding actually means, how it differs from traditional coding, some of the best vibe coding tools, and best practices.
Quick overview of vibe coding
Vibe coding is using tools like Cursor or Lovable to generate software applications by basic prompting. Basically you guide the AI tool with context, give it clear goals, and refine its output until it fits your intent.
A simple way to think about it is to imagine working with a junior developer. If you give vague instructions, you'll get vague results. But if you share a clear brief, define structure, and give feedback step by step, that developer quickly improves and starts producing useful work. AI tools behave the same way. They need direction, boundaries, and context.
What vibe coding is
- Translating intent into code. You guide AI with clear goals instead of typing everything yourself.
- Testing ideas fast. Use AI to explore options and iterate quickly.
- Building with structure. Move fast, but keep enough order to avoid a mess later.
- Amplifying your skills. AI helps you work faster, but your judgment still drives the outcome.
What vibe coding is not
- Copy-pasting code. Random snippets without context don't make a product.
- Letting AI "build an app" for you. It needs direction, not blind trust.
- Outsourcing decisions. You still need to make the technical calls.
- Magic without understanding. If you don't get what it's doing, you can't fix it when it breaks.
Vibe coding example workflow
Let's say you're building a small task management app.
You start by explaining your goal to an AI coding tool: a simple web app where users can create, update, and delete tasks. You don't just ask it to "build the app". Instead, you describe the structure, maybe React for the frontend, Appwrite for the backend, and a clean UI with a form and task list.
The AI generates a rough setup. You review it, make small edits, and ask follow-up questions like, like "Make the layout responsive". Each round of feedback gets it closer to what you want.
Next, you use the same tool to write a few tests or refactor a messy function. You keep checking the logic, adding context, and fine-tuning the results.
By the end, you've built a functional prototype faster than usual, and the code still makes sense to you because you stayed in charge throughout the process.
Vibe coding vs traditional coding
Traditional coding and vibe coding share the same goal: To build reliable software.
But the way you get there feels very different. In traditional coding, you handle every step yourself, from setup to syntax. With vibe coding, you still write and review code, but much of the heavy lifting is done by AI tools.
| Vibe coding | Traditional coding |
Starts with intent and context | Starts with writing code from scratch |
Collaborates with AI to explore solutions | Relies fully on human input and iteration |
Iterative, fast feedback cycles | Linear, step-by-step development |
Focuses on guiding, reviewing, and refining | Focuses on implementing everything manually |
Great for prototyping and experimentation | Better suited for fully defined systems |
Requires basic technical know-how | Requires deep technical understanding |
Vibe coding best practices
While vibe coding tools are great, they're only as good as the person guiding them. It's still real software development, with real bugs, dependencies, and trade-offs. It's important to know where AI helps and where you need to step in.
Know the basics: AI can generate code, but it can't reason about your system the way you can. When something breaks, and it often will, your understanding of the basics is what helps you debug, adapt, and move forward instead of getting stuck. A simple understanding of the fundamentals of frontend, backend, and databases, and how they work, can go a long way.
Stay aware of security: AI doesn't naturally think about secure practices or edge cases. It might expose keys, skip validation, or leave gaps you'd never accept in production. Always review code with a security mindset, even if it looks clean on the surface. We have covered the vibe coding security best practices in this blog.
Write clear prompts: The model only knows what you tell it. Vague prompts lead to vague results. Give it context, define structure, and explain what matters: style, dependencies, expected behavior. Clarity is half the work in vibe coding.
Bottom line, you should be the owner. AI can generate, suggest, and review, but it doesn't understand trade-offs. You decide what's acceptable, what's scalable, and what's worth rewriting. Smart vibe coders treat AI as an assistant, not a replacement.
Advantages and disadvantages of vibe coding
Like any shift in how we build software, vibe coding comes with trade-offs. It can make you faster, sharper, and more experimental, but only if you know its limits. Here's what it gets right, and where it can slow you down.
Advantages of vibe coding:
- Faster iteration: You can go from idea to working code in minutes. Perfect for prototyping, testing, or exploring new directions.
- Less boilerplate: Repetitive setup and syntax melt away, letting you focus on structure, flow, and user experience.
- Learning boost: For developers, it's like you get a pair programmer with ideally infinite patience. Just that you have to bear with "You're absolutely right." once in a while.
Disadvantages of vibe coding:
- Shallow understanding. It's easy to skip over what's actually happening under the hood, which can make debugging harder later.
- Security blind spots. AI won't always handle sensitive data or validation correctly. You need to review everything.
- Inconsistent quality. Results can vary based on how well you prompt or how complex the task is. It's not a set-and-forget process.
- Over-reliance. The more you depend on the model, the harder it becomes to think through problems independently.
Popular vibe coding tools
1. Lovable
Lovable is an AI app builder that leans on Supabase for backend services. It can generate UI and basic flows from prompts, but it does not automatically deliver a full app with database, auth, and APIs wired end-to-end. You'll need to connect your project to a Supabase database yourself, and that integration can be tricky depending on your schema, auth rules, and project setup.
It's useful for quick front-to-back prototypes where speed matters, but because it depends on external infrastructure, you may run into limits when you try to customize deeply or scale. Expect to take more control of the stack as your app grows.
2. Bolt
Bolt is a lightweight environment for rapid iteration. It generates small web apps quickly and lets you adjust the code as you go. It's helpful for exploring ideas, building quick demos, or learning through experimentation. It's not a long-term workspace. For larger apps, you'll outgrow it fast.
The setup is lightweight and smooth, though it depends on external services like Netlify for hosting. That makes it less suited for long-term or complex builds where you'd want deeper integration.
3. Replit
Replit offers a browser-based coding environment with AI assistance. It can generate, explain, and refactor code across multiple languages.
It's accessible. You can start building immediately without setup. It's solid for beginners or quick projects, but might not be a great option for performance-heavy codebases.
4. v0 by Vercel
v0 focuses on generating React-based UIs from text prompts. It's designed for front-end work: components, layouts, and interactions.
The generated code is clean and integrates easily into Next.js projects. It's limited to UI, though, so you'll still need to handle backend logic elsewhere.
Frequently asked questions (FAQs)
1. What is meant by vibe coding?
Vibe coding means building software with the help of AI coding assistants that understand natural language. Instead of typing every line yourself, you guide the AI through context, intent, and feedback, almost like mentoring a junior developer.
2. What are the benefits of vibe coding?
The biggest advantage is speed. You can move from an idea to working code much faster. It also helps reduce boilerplate work, improves iteration cycles, and acts as a learning companion that can explain or optimize code.
3. What is vibe coding vs prompt engineering?
Prompt engineering focuses on crafting precise inputs for AI models, mainly to get consistent or high-quality responses. Vibe coding goes beyond that. It's about using those prompts within a live development workflow: writing, testing, debugging, and shipping real software with AI collaboration.
4. Is vibe coding the future?
It's definitely shaping it. While traditional coding isn't going away, the tools we use and how we approach development are evolving. Vibe coding represents a shift toward human-AI collaboration, where developers act more as architects and reviewers, while AI handles repetitive or mechanical tasks.
Conclusion
While vibe coding tools are getting better, they still depend on how well you understand the problem, structure your ideas, and review the code written by AI.
If you treat AI like a partner instead of a shortcut, you'll get real value from it. The speed, flexibility, and creative flow are all there, as long as you stay in control of the work.
