What is vibe coding? Vibe coding is an AI-assisted way of building software where you describe what you want in plain English and an AI model generates, runs, and iterates on the actual code, rather than you writing it line by line. The term was coined by AI researcher Andrej Karpathy in February 2025 to describe fully giving in to the AI, prompting it, accepting suggestions, and barely reading the generated code, and it was named Collins Dictionary’s Word of the Year for 2025.
In practice, vibe coding follows a loop: you describe your goal, an AI model like GPT, Claude, or Gemini generates working code, you run the app and see what happens, and you describe the next change based on what you observe, largely without opening the code editor yourself. Vibe coding tools fall into two broad categories: AI app builders such as Lovable, Bolt.new, and Replit, which need no programming background at all, and agentic code editors such as Cursor and Claude Code, which work best when you already understand code.
Vibe coding has made app creation dramatically more accessible, but it also carries real risks, studies have found a large share of AI-generated code contains security vulnerabilities, which is why understanding both the opportunity and the risk matters before you build something real with it.
What is Vibe Coding?
Vibe coding is a way of building software where you describe your goal in natural language and an AI model handles the actual writing of the code, running it, and fixing it based on your feedback. Instead of learning syntax and writing every function yourself, you focus on describing outcomes, “build me a login page with email and password,” and let the AI translate that into working code.
Collins Dictionary’s official definition frames it as “the use of artificial intelligence prompted by natural language to assist with the writing of computer code.” Karpathy’s own framing goes further: with vibe coding, you “fully give in to the vibes” and can largely “forget that the code even exists.” That distinction matters, vibe coding isn’t just AI helping you code faster, it’s a shift toward not reading most of the code at all.
The shift echoes something already happening in search and retrieval: instead of matching exact syntax, both vibe coding and semantic search work by translating what you mean into what the system does, one for code, the other for information retrieval.
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Vibe Coding Meaning: Where Did the Term Come From?
The vibe coding meaning traces back to a single tweet. In February 2025, Andrej Karpathy, former Director of AI at Tesla and a founding member of OpenAI, described a new way he’d started building small apps: prompting an AI, accepting its suggestions, and moving forward on instinct rather than reading every line it produced.
The term spread fast. By November 2025, Collins Dictionary named vibe coding its Word of the Year, describing it as “programming by vibes, not variables,” and noting it reflects a broader cultural shift toward AI-assisted everything, not just software. The choice wasn’t uncontroversial; other words on the same shortlist, like “clanker” (a mocking term for AI), captured real skepticism about how far this shift should go, and that tension between excitement and caution is worth keeping in mind throughout this guide.
How Does Vibe Coding Actually Work?
Underneath the marketing, and now that you know what is vibe coding at a conceptual level, here’s how the loop actually runs in practice, regardless of which tool you use:
The Vibe Coding Workflow, Step by Step
- Describe the goal in plain language, through a chat interface or sometimes voice, e.g. “build a habit tracker with a streak counter.”
- The AI model generates the code, using a large language model trained on huge amounts of existing code and documentation.
- You run the app and observe the result, often inside the same tool, via a live preview.
- You describe the next change based on what you saw: a bug, a missing feature, a design tweak, and the AI iterates.
- You repeat steps 3–4 until the app does what you wanted, largely without opening or reading the underlying code.
That last point is what separates vibe coding from ordinary AI-assisted development: the goal isn’t just writing code faster, it’s staying in the loop of outcome and feedback while treating the code itself as a black box you don’t necessarily need to inspect.
Vibe Coding Prompt Examples: What Works and What Doesn’t
The quality of a vibe coding session depends heavily on how the prompt is written. Vague prompts produce vague apps; specific prompts produce something closer to what you actually pictured.
| Weak Prompt | Why It Struggles | Stronger Version |
| “Build me a social app” | Too broad; the AI has to guess almost everything | “Build a simple app where users post a photo and caption, and others can like it” |
| “Make the login better” | No definition of what “better” means | “Add a ‘forgot password’ link that emails a reset link to the user” |
| “Fix the bug” | Assumes the AI can see what you’re seeing | “When I click submit with an empty form, it crashes instead of showing an error” |
Notice the pattern: stronger prompts describe a concrete behaviour or scenario, not a vague adjective. This matters more in vibe coding than in traditional development, since the AI has no shared context with you beyond what’s in the conversation and the codebase it can see.
Vibe Coding vs Traditional Coding vs AI-Assisted Coding
These three terms get used loosely, but they describe genuinely different workflows:
| Approach | Who Writes the Code | Example Tools |
| Traditional coding | You, line by line | Any text editor, VS Code without AI plugins |
| AI-assisted coding | You, with inline AI suggestions | GitHub Copilot autocomplete |
| Vibe coding | The AI, from your natural-language prompts | Lovable, Cursor, Claude Code, Replit |
GitHub Copilot is the clearest edge case: it’s excellent at inline code completion, but it doesn’t generate a full application from a description the way a dedicated vibe coding tool does. Most practitioners treat Copilot as AI-assisted coding, a different, narrower category, rather than true vibe coding.
AI for Coding: The Broader Landscape
Vibe coding is one specific workflow inside a much bigger shift in AI for coding generally. Every vibe coding tool is a wrapper around an underlying code-generating AI model, large language models like GPT, Claude, and Gemini that were trained on enormous amounts of public code and technical documentation, much of it from open-source models and repositories hosted on platforms like Hugging Face, and can predict what code should come next given a description or partial file.
What changed between 2023 and 2026 wasn’t just that these code-generating AI models got smarter, it’s that the tooling around them matured: live previews, one-click deployment, database and authentication scaffolding, and agentic behaviour, where the AI can run commands, read error messages, and self-correct, rather than just suggesting a snippet and waiting for you.
Vibe Coding Tools: The Complete Landscape for Beginners
Vibe coding tools split cleanly into two categories, and picking the wrong one for your skill level is the single most common beginner mistake.
AI App Builders (No Prior Coding Needed)
These tools generate a complete, running application, front end, backend, and often a database, from a conversation. You never need to open a code editor, which is what makes this end of AI for coding so approachable for complete beginners.
| Tool | Best For | Watch Out For |
| Lovable | Full-stack apps with the lowest barrier to entry | Database security rules must be checked before launch |
| Bolt.new | Fast, prompt-to-app scaffolds you can refine | Often needs a stronger backend for production use |
| Replit | Learning while shipping, all-in-one cloud IDE | Public projects can expose source code and secrets |
| v0 (Vercel) | Polished React/Next.js interface components | Frontend only — no database, auth, or backend logic |
Agentic IDEs and Code Editors (Some Coding Knowledge Helps)
These tools live inside a real development environment and work with an existing codebase. You’ll get significantly better results if you can read the code they produce, even if you’re not writing much of it yourself. For genuinely multi-step, stateful agent workflows rather than single-file edits, some teams pair these editors with a dedicated orchestration layer like our LangGraph tutorial walks through, or run models locally through a tool like Ollama when privacy or offline access matters.
| Tool | Best For | Notable Trait |
| Cursor | Serious, hands-on vibe coding with deep codebase context | The editor Karpathy used when he coined the term |
| Claude Code | Terminal-first, multi-file agentic changes | Strong for larger, more complex codebases |
| GitHub Copilot | Inline suggestions inside an existing workflow | AI-assisted coding, not full app generation |
A useful rule of thumb: if you have zero coding background, start with an AI app builder like Lovable. If you already understand code and want to move faster inside it, an agentic editor like Cursor or Claude Code will serve you better.
Building Your First App with Vibe Coding: A Beginner’s Walkthrough
Here’s what a first project realistically looks like, using an AI app builder as the example:
- Pick a small, well-scoped idea. A to-do list, a habit tracker, or a simple booking form, not a full marketplace or social network.
- Describe it in one clear prompt: what the app does, who uses it, and the two or three core features it needs, nothing more.
- Review the live preview the tool generates and click through it like a real user would, not just glance at it.
- Prompt for one change at a time. “Add a delete button to each item” works better than five requests bundled into one message.
- Once it works, read through the parts of the code you can understand, especially anything touching passwords, payments, or personal data, before sharing the app with anyone else.
That last step is easy to skip and is exactly where most beginner vibe coding projects run into trouble later.
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Why Vibe Coding Matters Now
Understanding what is vibe coding matters because the barrier to building software has genuinely collapsed for a huge number of people. Lovable’s annualised revenue grew from roughly $100 million to $400 million in about six months, largely driven by non-technical founders shipping real, paying products by describing them in plain English. Cursor’s parent company, Anysphere, was reported at a $9.9 billion valuation as agentic coding tools scaled through 2025 and 2026.
Adoption among professional developers tells a similar story: Stack Overflow’s 2025 Developer Survey found 84% of professional developers were either actively using or planning to use AI coding tools. That number matters for anyone thinking about a software career today, this isn’t a fringe experiment anymore, it’s becoming baseline professional practice.
This isn’t limited to solo builders and startups, either. A JetBrains developer survey conducted in early 2026 found that more than a third of enterprise development teams were already using AI to generate substantial blocks of code directly from natural language prompts, not just accepting autocomplete suggestions here and there. Enterprise adoption at that scale is usually the clearest signal that a workflow has moved from novelty to standard practice.
The Risks of Vibe Coding: What Beginners Need to Know
The same Stack Overflow survey found something worth sitting with: even as adoption climbed, developer trust in AI accuracy fell from 40% to 29% year over year. High usage and low trust are both true at once, and the data on AI-generated code backs up that caution.
- Security vulnerabilities are common. Independent research testing AI-generated code found roughly 45% of samples introduced OWASP Top 10-style vulnerabilities, the same vulnerability class security teams have flagged as critical for over a decade.
- Real production apps are affected, not just test projects. A large-scale scan of thousands of publicly deployed vibe-coded applications found thousands of critical vulnerabilities and hundreds of exposed API keys and access tokens in apps already being used by real people.
- Technical debt accumulates faster. A study of millions of pull requests found technical debt rising roughly 30–41% after teams adopted AI coding tools without adjusting their review process.
- The most common beginner mistake is a database left wide open. Tools like Lovable generate a backend automatically, and forgetting to lock down its access rules is the single most repeated security gap across vibe-coded apps.
- Trust hasn’t kept pace with reliance. Developers increasingly assume AI-generated code is secure by default, when independent testing consistently shows the opposite is closer to true, which is precisely the gap that leads to unreviewed code reaching production.
None of this means vibe coding is unsafe to learn or experiment with. It means treating AI-generated code the same way you’d treat code from any other source you didn’t personally write: worth verifying before it touches real user data or goes live.
Vibe Coding Best Practices for Beginners
- Start with a throwaway project. Learn the tool’s quirks on something with no real users before building anything that matters.
- Never leave secrets in the code the AI shows you. API keys and passwords should live in environment variables, not hardcoded into a file.
- Check your database rules before sharing a link. If your tool uses Supabase or a similar backend, confirm row-level security is actually turned on.
- Prompt for one change at a time. Small, specific requests are easier for the AI to get right, and easier for you to verify afterward.
- Read the code you can understand, even if you can’t read all of it. Partial review is still better than none, especially around auth and payments.
- Get a second pair of eyes before launch. If the app will handle real user data or payments, have someone with development experience review it first.
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Who Should (and Shouldn’t) Rely on Vibe Coding?
Vibe coding is a strong fit for prototyping an idea quickly, validating whether people want a product before investing months in it, and learning the shape of how applications work. It’s a weaker fit the moment real user data, payments, or compliance requirements enter the picture, at that point, the code needs the same review any production system would get, regardless of who or what wrote it.
A pattern that shows up repeatedly among non-technical founders: use a vibe coding tool to build something you can demo to a real developer, then bring that developer in to harden it for production. That approach captures the speed of vibe coding without inheriting all of its risk.
Students and career-switchers sit in a slightly different position: vibe coding is genuinely useful for seeing how a full application fits together end to end, something that’s hard to grasp from isolated coding exercises. The caveat is the same one that applies everywhere else in this guide: treating the AI’s output as something to learn from, not just something to accept, is what turns a vibe-coded side project into an actual skill.
Vibe Coding and the Future of Software Careers
Vibe coding is changing what early-career software work looks like, not eliminating it. A 2025 industry survey found 54% of engineering leaders planning to hire fewer junior developers specifically because of AI efficiency gains, which sounds alarming until you notice the follow-on problem: senior engineers are made, not born, and they’re made by debugging real systems for years, exactly the work AI is now absorbing.
The practical takeaway for anyone building a software career right now is that understanding what the AI is generating, why it works, and where it fails, is becoming more valuable, not less. Fluency with tools like Cursor and Claude Code, paired with real software engineering fundamentals, data structures, system design, security, is what separates someone who can direct AI-generated code from someone who can only accept it. If you want to build that fluency properly, with mentorship and real projects rather than piecing it together from scattered tutorials,
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What is vibe coding, in short? It's describing an app in plain English and letting AI write, run, and fix the code, a workflow that's gone from a Karpathy tweet in February 2025 to Collins Dictionary's Word of the Year in under a year. It has made building software radically more accessible, but the data on security and technical debt is a genuine reason for caution, not just hype. Used well, for prototyping, learning, and validating ideas fast, followed by real review before anything goes live, vibe coding is one of the most useful additions to a beginner's toolkit in years.
Scaler's Modern Software and AI Engineering Program combines strong software fundamentals with AI-assisted development, debugging, code review, and real-world product building, with hands-on projects and 1:1 mentorship.
Frequently Asked Questions
What is vibe coding?
Vibe coding is describing an app in natural language and letting an AI model write, run, and fix the code, instead of coding it yourself line by line.
Who coined the term vibe coding?
AI researcher Andrej Karpathy coined vibe coding in February 2025, and Collins Dictionary named it Word of the Year 2025.
What does vibe coding meaning actually cover?
It covers the full loop of prompting, running, and iterating on AI-generated code, not just getting AI help while you write code yourself.
Is vibe coding safe for beginners?
It's safe for learning and prototyping, but research shows a meaningful share of AI-generated code has security flaws, so review before real-world use.
Which vibe coding tools should a complete beginner start with?
Lovable, Bolt.new, or Replit are the easiest starting points since they need no prior coding knowledge at all.
Is GitHub Copilot considered a vibe coding tool?
Not exactly. Copilot offers inline code suggestions, a form of AI for coding, but doesn't generate full apps from a description like true vibe coding tools do.
Can vibe coding replace professional software developers?
Not fully. It replaces a lot of manual typing, but reviewing, securing, and architecting real systems still needs human software engineering skill.
Is vibe coding a good way to learn programming?
It's a good way to learn what's possible and validate ideas fast, but reading and understanding the generated code is still essential for real learning.
