What is Vibe Coding and How Does AI Coding Work?

Software development is going through one of the biggest transformations in recent years. With the rapid growth of Artificial Intelligence (AI), the way developers create software is changing from writing every single line of code manually to simply describing what they want and letting AI help build it.

In early 2025, Andrej Karpathy, a renowned AI researcher, former leader of Tesla’s Autopilot team, and co-founder of OpenAI introduced a new way of thinking about software creation. He shared how he was experimenting with a different approach: instead of carefully writing code line by line, he would communicate his ideas to an AI using simple natural language, review the results, and continue building. He described this experience as “fully giving into the vibes”  a phrase that inspired the term vibe coding.

So, what is vibe coding? It is a new approach to software development where users interact with AI coding tools through natural language prompts instead of manually creating every part of an application. AI models can generate code, suggest improvements, fix errors, and help transform ideas into working software much faster.

Today, AI-powered platforms such as Cursor, GitHub Copilot, Claude, Gemini, and Replit are making AI coding more accessible for both professional developers and beginners. These tools allow users to create applications, automate tasks, and experiment with software ideas by simply explaining what they want to build.

While many people see vibe coding as the future of software development, others highlight concerns around code quality, security, and the importance of human review. The reality is that vibe coding is not about replacing developers, it is about changing how people build software by combining human creativity with the power of AI.

What is Vibe Coding?

 Vibe coding is a way of building software where you describe what you want in plain English (or any natural language), and an AI writes the code for you. Instead of worrying about syntax, libraries, or how to structure a function, you focus entirely on the outcome — what should the app do, how should it behave, what problem should it solve.

The term comes from Karpathy’s February 2025 description of building a small project called MenuGen using Cursor Composer and voice input. His insight was simple: modern AI models had gotten good enough that you didn’t have to understand the code they wrote. You could just run it, see if it worked, and ask for changes if it didn’t. He described it as “almost forgetting that the code even exists.”

That framing resonates because it captures something genuinely new. Traditional programming requires you to think like a computer — to translate your idea into the precise grammar a compiler will accept. Vibe coding flips that. The AI thinks like a computer. You just need to think clearly about what you want.

“It’s not about lowering your standards. It’s about changing what skill you’re applying.” a useful way to reframe it

Vibe coding works for both total beginners who have never written a line of code and for experienced developers who want to move faster on projects they understand deeply. The workflows look different, but the core idea is the same: describe the outcome, let the AI handle the implementation.

How Vibe Coding Actually Works: The Loop

In practice, vibe coding follows a tight four-step cycle that repeats until you have something you’re happy with.

Describe → Generate → Test → Refine.

You start with a prompt. The AI produces code. You run it and see what happens. Then you either accept it or write a new prompt that tells the AI what to fix. This cycle takes minutes instead of hours. For simple features, you might loop three or four times. For a complex module, maybe twenty.

Here’s what that looks like with a real example. Say you want to build a form that captures email addresses and saves them to a Google Sheet.

A beginner might write: “Make a form that collects emails.”

That will produce something, but it’ll be generic probably just the HTML with no backend logic, no error handling, no connection to anything useful.

A better prompt: “Build a simple email capture form in React with a single input field and a Submit button. On submit, it should POST the email to an API endpoint at /api/subscribe. Show a success message if the request succeeds, and a ‘Something went wrong, try again’ message if it fails. Keep the styling minimal — white background, one centered column, mobile-friendly.”

Same goal. Completely different output quality.

This is the thing most introductory vibe coding articles skip: the quality of what you get out is almost entirely determined by the quality of what you put in. More on that in the next section.

One more concept worth knowing here: vibe deploying. Vibe coding doesn’t have to stop at writing the code. Tools like Google’s Firebase Studio and some Cursor workflows now let you deploy your app to a live environment with a single command or click no DevOps knowledge required. For someone building a side project or an internal tool, this removes what used to be a significant bottleneck.

Writing Prompts That Actually Work

Every article about vibe coding tells you to “describe your goal in plain language.” None of them tell you what a good description actually looks like.

Here’s the honest truth: prompt writing is the new syntax. In traditional coding, the skill was translating ideas into code your compiler would accept. In vibe coding, the skill is translating ideas into instructions your AI will interpret correctly. It’s a different skill  but it’s still a skill. It doesn’t come automatically.

Three prompt patterns that consistently produce better results:

Pattern 1: Goal + Constraints Don’t just say what you want. Say what you want and what you don’t want. “Build a pricing table with three tiers. Use Tailwind CSS. No animations. Each tier should have a title, a monthly price, three bullet points, and a CTA button. The Pro tier should be visually highlighted.”

Pattern 2: Goal + Persona + Output format Tell the AI who it’s building for and what format the output should take. “I’m building an internal tool for a small HR team with no technical background. Create a simple CSV upload form that validates the file has the columns ‘Name’, ‘Email’, and ‘Department’ before processing. Return a user-friendly error message in plain English if validation fails no jargon.”

Pattern 3: The refinement prompt When the first output is close but not right, don’t scrap it and start over. Build on it. “That’s close. Keep the layout, but change the button color to match the header. Also, the mobile view is breaking the two columns are overlapping below 480px. Fix just the responsive layout without changing anything else.”

That last pattern matters more than people realize. New vibe coders often throw away a nearly-correct output because they don’t know how to describe the gap. Learning to articulate “what’s almost right but wrong” is one of the highest-leverage skills you can develop here.

Think of it this way: you’re briefing a contractor who is extremely fast, never complains, has no opinions, but will do exactly what you said not what you meant. Write prompts the way you’d write a spec, not the way you’d say something to a colleague.

Who Should Use Vibe Coding?

“Anyone can use vibe coding” is technically true but not especially helpful. Here’s a more honest map of who gets the most out of it, what they can realistically build, and where their limits are.

Designers are probably the biggest hidden beneficiaries. If you’ve ever had an idea stuck in Figma for three weeks waiting for a dev handoff, vibe coding changes your timeline from weeks to hours. Tools like Lovable are built specifically for this workflow. The skill floor is low if you can describe a component in design terms, you can build it. The ceiling: anything that needs a real backend or database gets complicated quickly.

Product managers can use vibe coding to build the internal tools they’ve been asking engineering to prioritize for months. Dashboards that aggregate data from two spreadsheets. Status trackers. Simple reporting tools. Jira still won’t let you have that custom view you’ve wanted for years but you can build it yourself now. Skill floor: moderate. You need to understand your data well enough to describe what you want clearly.

Entrepreneurs and solopreneurs can use vibe coding to test product ideas without hiring a developer. A landing page with a waitlist form, a simple booking flow, a basic marketplace prototype these are all within reach. The important caveat: validate the idea first, then bring in real engineering when you’re building for scale.

Domain experts — doctors, CAs, teachers, lawyers have a huge opportunity that most vibe coding coverage ignores. If you understand a workflow deeply, you can describe it precisely. A CA who knows exactly how a tax computation works can build an internal tool to automate it faster than explaining the logic to a developer ever would. The skill floor is higher here you need patience with the refinement loop but the output can be genuinely specialized.

Experienced developers get the largest raw productivity boost. Boilerplate that used to take an afternoon takes ten minutes. The skill floor is already there; vibe coding just removes the parts of the job that were never intellectually interesting anyway.

The Context Window Problem: What Breaks at Scale

This is the most important section in this article for anyone planning to build something beyond a quick prototype.

Every AI model has a context window the amount of text it can “see” at one time. In a vibe coding session, that window fills up with your conversation history, the code it’s already written, and your latest instructions. Once the window is full, older content falls out. The AI effectively forgets it.

In practice, this creates a problem that experienced vibe coders call context drift. Your AI builds an auth system in Session 1 using a specific library and a specific approach. In Session 15, it’s generating new code that conflicts with those earlier decisions — because it can’t see them anymore. For a non-developer who can’t read the code carefully, this drift is invisible until something breaks in a confusing way.

Three practical fixes that actually work:

1. Keep an architecture.md file. A plain text file that describes your project’s key decisions: what stack you’re using, what libraries handle what, how auth works, how data is structured. Paste this into your context at the start of every new conversation. It costs you thirty seconds and prevents hours of confusion.

2. Use .cursorrules or equivalent. Cursor lets you write persistent instructions that apply to every prompt in a project. Other tools have similar features. Use this for: your stack preferences, your naming conventions, your testing requirements, your deployment target. The AI reads this every time.

3. Know when to start a fresh conversation. A general rule: if a session has gone on so long that the AI is starting to contradict itself, start fresh with a short summary prompt: “Here’s the project state and what I’m about to build next…” Continuity beats length when the window is full.

This problem gets worse the more complex your project is. It’s one of the main reasons vibe coding is excellent for focused, bounded tasks a new component, a specific feature, a standalone script and gets harder as a project grows.

When NOT to Vibe Code: An Honest Decision Framework

Most vibe coding content is enthusiastic to the point of being misleading. Here’s the other side.

Security-critical surfaces. Authentication, authorization, payment flows, session management these are areas where a subtle flaw can be catastrophic. AI models write plausible-looking security code that is often wrong in non-obvious ways. Unless you have a developer who can audit the output carefully, don’t vibe code your login system.

Complex state management. AI tends to produce code that works for the happy path and handles edge cases poorly. For applications with complex state — real-time collaboration, intricate form logic, multi-step transactions — the AI’s solutions are often readable but fragile.

Team-maintained codebases. AI-generated code is frequently inconsistent. Different prompts produce different styles, different abstractions, different conventions. If three developers are all vibe coding into the same repo, the codebase becomes incoherent quickly. Without an opinionated review process, technical debt accumulates faster than you might expect.

Regulated industries. Healthcare apps, fintech tools, edtech platforms these operate under compliance requirements (HIPAA, PCI-DSS, DPDP in India) where the source of your code matters legally. This is an area where liability questions remain genuinely unresolved, and the stakes of getting it wrong are high.

The 5-question checklist before you vibe code something:

1. Will someone’s data or money be at risk if this has a subtle bug?
2. Does this need to be maintained by a team for more than six months?
3. Is performance a hard requirement, not just a nice-to-have?
4. Does this touch auth, payments, or user permissions?
5. Is this in a regulated industry with compliance requirements?

If you answered yes to two or more, vibe coding is the wrong tool. It doesn’t mean AI can’t help — it means you want AI-assisted engineering: AI as a collaborator that writes drafts, and a human who understands the code fully before it ships.

This distinction — between vibe coding and AI-assisted engineering was articulated clearly by Addy Osmani, an engineering leader at Google. His point: conflating the two gives newcomers a dangerously incomplete picture of what professional software development requires. A team doing TDD, code review, and design documents isn’t vibe coding even if they use Copilot all day.

Vibe Coding vs. Traditional Coding vs. AI-Assisted Engineering

Here’s the clearest breakdown of the three modes:

The crucial point in that last row: in pure vibe coding, you might ship code you can’t read. For a weekend project or an internal tool used by three people, that’s often an acceptable tradeoff. For production software at scale, it’s a serious risk.

The model that works best for most professionals is what daily.dev calls “Vibe & Verify” — AI handles the routine implementation, and you review anything that touches critical paths. Fast where speed is safe, careful where care is required.

What Vibe Coding Means for Developers’ Careers

The honest answer to “will vibe coding take my job” is: it depends entirely on what your job actually involves.

41% of global code being AI-generated sounds alarming. But what it really means is that the kind of code developers spend time on is shifting. Less time writing CRUD routes and authentication boilerplate. More time on architecture, system design, code review, and the judgment calls that AI genuinely can’t make — yet.

The skills that are gaining value right now:

• System design and architecture (understanding how to structure something, not just how to write it)
• Prompt engineering as a professional discipline
• Code review — especially reviewing AI-generated code, which fails in characteristic ways
• Domain expertise paired with technical communication — knowing enough to describe complex requirements precisely

The skills that are commoditizing:

• Writing boilerplate from scratch
• Basic CRUD implementation
• Routine UI components
• Repetitive test scaffolding

For developers in India specifically, this has a particular texture. India’s software industry has long been built on a large pool of skilled developers executing implementation work for global clients. As AI takes over more of that execution layer, the value shifts toward people who can design systems, own outcomes, and communicate precisely about complex requirements. Junior developers who learn to use AI tools well — rather than competing with them — are significantly better positioned than those who don’t.

The developer who disappears is not the competent one. It’s the one who refused to adapt.

How to Get Started with Vibe Coding Today

Here’s a straightforward path that actually works, whether or not you’ve ever written code before.

Step 1: Pick one tool and ignore the rest for now. If you’ve never coded: start with Bolt.new — it runs in the browser, no setup required. If you’re a developer: start with Cursor. Install it, open an existing project, and use it for your next feature.

Step 2: Start with a throwaway project. Your first vibe coding session should be something with zero stakes — a personal portfolio page, a simple expense tracker, a tool that does something useful just for you. The goal is to learn the loop, not to ship something impressive.

Step 3: Write your first prompt using the goal + constraints format. Don’t just say “make a todo app.” Say: “Build a simple todo list app in React. Tasks should have a title and a done/not-done status. Include the ability to add a new task, mark a task as done, and delete a task. Styling should be clean and minimal — white background, no animations. No backend needed; store tasks in local state.”

Step 4: Run the output, then refine. Don’t scrap what the AI made because it’s not perfect. Run it. See what’s wrong. Write a refinement prompt that describes the specific gap. You’ll learn faster in iteration than in restarts.

Step 5: Learn to read the code, even if you can’t write it. You don’t need to become a developer. But learning to read AI-generated code — to understand roughly what it’s doing, where the data is, what the main functions are called — will dramatically improve your ability to give better prompts and catch obvious problems. Most AI tools will explain any piece of code if you just ask.

Good first project ideas:

• A personal portfolio page with your bio and links
• A simple expense tracker that calculates totals
• A lead capture landing page for a project you’re working on
• A habit tracker that uses local storage
• An internal tool that automates something you do manually in spreadsheets

Conclusion

Vibe coding represents one of the most significant shifts in software development since the introduction of modern programming frameworks.

By combining Artificial Intelligence, Large Language Models, and advanced AI code generation capabilities, developers can build applications faster than ever before. At the same time, organizations must balance speed with security, maintainability, and long-term scalability.

For startups, product teams, and developers looking to accelerate innovation, vibe coding offers enormous opportunities. However, success still depends on human judgment, careful testing, and a solid understanding of software engineering principles.

The future of software development is unlikely to be fully human or fully AI. Instead, it will be shaped by effective collaboration between people and intelligent systems—and vibe coding is one of the clearest examples of that transformation already happening today.

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