What Is Vibe Coding? The Trend Changing How Developers Build Software

You sit down at your keyboard, open a chat window to an AI coding assistant, and type: “Build me a REST API with user authentication, database integration, and rate limiting.” Twenty seconds later, you have a working application. You didn’t write a single line of code yourself. Welcome to vibe coding — the development methodology that’s reshaping how software gets built in 2026.

Vibe coding is more than a buzzword. It represents a fundamental shift in the relationship between developers and their tools, where natural language replaces syntax as the primary interface for building software. But is it a genuine paradigm shift, or just a fancy way of saying “I let the AI do it”? In this guide, I’ll break down exactly what vibe coding is, where it came from, when it works, when it doesn’t, and how to get started.

What Is Vibe Coding? A Clear Definition

Vibe coding is a software development approach where you describe what you want to build in natural language and let an AI coding assistant handle the actual implementation. Instead of manually writing every function, class, and configuration file, you communicate your intent — the vibe of what you’re building — and iterate on the AI’s output through conversation.

In practical terms, a vibe coding session looks something like this:

1. You describe a feature, component, or entire application in plain English
2. An AI assistant generates the code — often across multiple files
3. You review the output, test it, and provide feedback
4. The AI refines the code based on your feedback
5. You repeat until the result matches your vision

The key distinction from simply “using AI autocomplete” is that vibe coding treats the AI as the primary author of the code. The developer’s role shifts from writing code to directing, reviewing, and refining it. You’re the architect and the code reviewer. The AI is the typist.

“The most enjoyable form of programming I’ve ever experienced. You just see stuff, say stuff, run stuff, and copy-paste stuff. I barely even look at the code. I just see things, say things, and run things — and it mostly works.” — Andrej Karpathy, describing vibe coding

Where Did the Term “Vibe Coding” Come From?

The term “vibe coding” was coined by Andrej Karpathy — the former director of AI at Tesla and co-founder of OpenAI — in a post on X (formerly Twitter) in February 2025. Karpathy described his experience of building software by simply talking to an AI, barely reading the generated code, and relying on the AI to debug errors when things broke.

His original description resonated with thousands of developers who had quietly started doing the same thing. The term stuck because it perfectly captured the feeling of this new workflow: you code based on vibes — on intent and direction rather than syntax and implementation details.

By mid-2025, “vibe coding” had entered the mainstream developer vocabulary. Conference talks, blog posts, and even job descriptions started referencing it. Some companies began distinguishing between “traditional coding” and “vibe coding” in their engineering practices, acknowledging that different tasks call for different approaches.

What made the term so sticky is that it’s honest about what’s happening. You’re not meticulously engineering every line — you’re going with the vibe, trusting the AI to handle the details, and intervening only when something feels off. Whether that’s brilliant or terrifying depends on the context, which we’ll get into later.

How Vibe Coding Actually Works in Practice

Let’s move past the theory and look at what a real vibe coding session looks like. Here’s an example of building a complete feature using Claude Code in the terminal:

Example: Building a Webhook System

You type this into your AI assistant:

Build a webhook delivery system for our Node.js API. Requirements:
- Users can register webhook URLs through a REST endpoint
- When events occur, queue webhook deliveries using Bull/Redis
- Implement exponential backoff retry (max 5 attempts)
- Log all delivery attempts with status codes
- Add a /webhooks/deliveries endpoint to check delivery history
- Include proper TypeScript types throughout

The AI then generates multiple files:

// Generated files:
src/webhooks/webhook.routes.ts     // REST endpoints for registration + history
src/webhooks/webhook.service.ts    // Business logic and queue management  
src/webhooks/webhook.worker.ts     // Bull queue processor with retry logic
src/webhooks/webhook.types.ts      // TypeScript interfaces
src/webhooks/webhook.model.ts      // Database model (Prisma schema additions)
tests/webhooks/webhook.test.ts     // Integration tests

You run the code, hit an error with the Redis connection, and say:

The Redis connection is failing because we use a custom Redis config 
from src/config/redis.ts — use the existing getRedisClient() function 
instead of creating a new connection.

The AI fixes the code, properly importing your existing Redis configuration. You test again, it works, and you move on. The entire feature took 15 minutes instead of the 2-3 hours it might have taken writing everything manually.

The Conversation Loop

Effective vibe coding isn’t a single prompt — it’s a conversation. The typical pattern looks like:

• Initial prompt: Describe the feature at a high level with key requirements
• First review: Check the generated architecture and catch major issues
• Refinement: “Change the error handling to use our custom AppError class” or “Add input validation with Zod”
• Integration: “This needs to work with our existing auth middleware — import it from src/middleware/auth.ts”
• Polish: “Add JSDoc comments to the public functions and improve the error messages”

Each round narrows the gap between what the AI generated and what you actually need. Good vibe coders develop an instinct for what to specify upfront versus what to fix in iteration.

Tools That Enable Vibe Coding

Not every AI tool is equally suited for vibe coding. The approach works best with AI assistants that understand project-wide context, can edit multiple files, and support iterative conversation. Here are the tools that define the vibe coding ecosystem in 2026:

Claude Code

Claude Code is arguably the purest vibe coding tool available. It runs directly in your terminal, understands your entire project through direct file system access, and can create, edit, and delete files autonomously. Because it operates at the project level rather than the file level, it’s particularly strong at generating features that span multiple files and directories.

What makes Claude Code excel at vibe coding is its deep context window. You can describe complex, multi-component features and it will generate coherent code across your entire project — respecting existing patterns, imports, and conventions. It’s the closest thing to having a senior developer who can read your entire codebase and implement features on command.

Cursor

Cursor’s Composer mode is the IDE-based answer to vibe coding. You describe what you want in the Composer panel, and Cursor generates or modifies code across multiple files within a visual editor. The advantage over terminal-based tools is that you can see changes in real-time, with syntax highlighting and inline diffs.

Cursor’s strength is in the review phase of vibe coding. The diff view makes it easy to inspect what the AI changed, accept or reject individual modifications, and refine the output. If you prefer a visual workflow over terminal-based development, Cursor is the leading option.

Windsurf

Windsurf’s Cascade system brings a similar agentic approach inside a VS Code-compatible editor. It can autonomously navigate your project, plan multi-step changes, and execute them. The Memories feature adds persistent context across sessions, so it learns your project’s conventions over time — making each vibe coding session more accurate than the last.

GitHub Copilot

GitHub Copilot has evolved significantly from its autocomplete origins. The Workspace feature in Copilot now supports multi-file edits and natural language feature requests, making it viable for vibe coding workflows. It’s not as powerful as Claude Code or Cursor’s Composer for complex multi-file tasks, but it’s the most accessible entry point since it integrates directly into VS Code without requiring a separate editor.

Quick Comparison for Vibe Coding

When Vibe Coding Works Well

Vibe coding isn’t universally applicable — it excels in specific scenarios and falls flat in others. Understanding when to vibe code and when to hand-write code is one of the most important skills for a modern developer.

Prototyping and MVPs

This is where vibe coding absolutely shines. When you’re exploring an idea, building a proof-of-concept, or racing to get an MVP in front of users, the speed of vibe coding is transformative. You can go from idea to working prototype in hours instead of days. The code might not be perfect, but it’s functional, and that’s what matters at this stage.

Boilerplate and CRUD Operations

Every developer knows the pain of writing yet another set of CRUD endpoints, database migrations, form validations, and API response handlers. Vibe coding eliminates this tedium entirely. Describe your data model and the AI generates the entire boilerplate stack — routes, controllers, services, models, and tests. This is arguably the highest-ROI use case for vibe coding because the code is straightforward and the risk of subtle bugs is low.

Personal Projects and Internal Tools

When the stakes are lower — a personal side project, an internal dashboard, a data processing script — vibe coding lets you build things you might never have gotten around to otherwise. That analytics dashboard you’ve been meaning to build for months? Describe it and have it running in an afternoon. The quality bar for internal tools is different from production software, and vibe coding fits perfectly.

Learning and Exploration

Vibe coding is an incredibly effective learning tool. Want to understand how WebSockets work? Ask the AI to build a real-time chat application and study the generated code. Curious about GraphQL? Have the AI scaffold a complete GraphQL API and examine the resolver patterns. You learn by reading and modifying working code rather than starting from abstract tutorials.

Frontend UI Development

Describing a UI layout and having it generated is one of vibe coding’s most satisfying use cases. “Build a responsive dashboard with a sidebar navigation, a data table with sorting and pagination, and a chart showing monthly revenue” produces a working UI that you can then refine visually. The iteration cycle of “make the sidebar darker,” “add hover effects to the table rows,” “move the chart above the table” is fast and intuitive.

When Vibe Coding Fails

Knowing when NOT to vibe code is just as important as knowing when to use it. Here are the scenarios where you should keep your hands on the keyboard:

Security-Critical Code

Authentication systems, encryption implementations, access control logic, and anything that handles sensitive data should not be vibe coded without extremely careful review. AI models can generate code that looks correct but contains subtle security vulnerabilities — improper token validation, SQL injection vectors disguised by ORM usage, or race conditions in permission checks.

If you vibe code security-related features, treat the AI’s output as a rough draft that requires the same scrutiny as a junior developer’s pull request. Every line needs review.

Performance-Sensitive Systems

When microseconds matter — high-frequency trading systems, game engines, real-time audio processing, database query optimization — vibe coding produces code that’s “correct but slow.” AI models optimize for readability and correctness, not for cache-line alignment, memory allocation patterns, or algorithmic efficiency at scale. These are areas where deep expertise and manual optimization still dominate.

Complex Distributed Systems

Distributed systems involve subtle coordination problems — consensus protocols, eventual consistency, partition tolerance, distributed transactions — that AI models struggle with. The generated code might handle the happy path but miss edge cases around network partitions, clock skew, or partial failures. These are problems where you need to understand every line of code, not just review it.

Highly Regulated Environments

In healthcare, financial services, aviation, and other regulated industries, you often need to demonstrate that every line of code was intentionally written and reviewed. Vibe coding’s “generate and review” approach may conflict with compliance requirements that demand detailed justification for implementation choices. While the code itself can be identical, the process documentation differs significantly.

Debugging Complex Issues

Ironically, while AI is good at generating code, it’s often mediocre at debugging complex production issues. When you’re chasing a race condition that only appears under load, or tracking down a memory leak in a long-running process, the systematic debugging skills of an experienced developer outperform the pattern-matching approach of AI assistants. Vibe coding is a building tool, not always a debugging tool.

The Skills Shift: What Developers Need to Learn

Vibe coding doesn’t eliminate the need for developer skills — it transforms which skills matter most. Here’s what’s changing:

Prompt Engineering for Code

The ability to clearly describe what you want — with the right level of detail, constraints, and context — is the core skill of vibe coding. Vague prompts produce vague code. Specific, well-structured prompts produce code that’s close to production-ready on the first try.

Compare these two prompts:

// Weak prompt (vague, no constraints):
"Add user authentication to my app"

// Strong prompt (specific, contextual, constrained):
"Add JWT-based authentication to our Express API. Requirements:
- Use bcrypt for password hashing (cost factor 12)
- Access tokens expire in 15 minutes, refresh tokens in 7 days
- Store refresh tokens in Redis, not the database
- Use our existing User model from src/models/user.ts
- Add middleware that extracts and validates the JWT from the Authorization header
- Return 401 for missing tokens, 403 for expired tokens
- Include rate limiting on the /auth/login endpoint (5 attempts per minute)"

The second prompt gives the AI enough context to generate code that fits your existing architecture and meets your security requirements. This specificity is a learned skill — one that improves dramatically with practice. For a deeper dive, see our guide on writing better AI prompts for coding.

Code Review and Pattern Recognition

When the AI writes the code, your job shifts to reviewing it. This requires the ability to quickly scan generated code and identify:

• Security issues: Unsanitized inputs, improper auth checks, exposed secrets
• Performance problems: N+1 queries, unnecessary re-renders, missing indexes
• Architectural violations: Code that breaks your project’s patterns or principles
• Missing edge cases: Error handling, empty states, boundary conditions
• Dependency issues: Using outdated libraries, unnecessary dependencies, version conflicts

Paradoxically, reviewing AI code requires more expertise than writing it yourself. When you write code line by line, you make conscious decisions at each step. When you review generated code, you need to verify decisions someone else (the AI) made — which requires understanding why each decision is correct or incorrect. For more on this topic, check out our AI coding best practices guide.

Architecture and System Design

Vibe coding handles implementation well but still needs human guidance on architecture. Deciding what to build, how components should interact, which patterns to use, and how to structure a system for scalability — these remain fundamentally human responsibilities. As vibe coding handles more of the “how,” the “what” and “why” become the developer’s primary value-add.

Developers who thrive in the vibe coding era will be those who can think at the system level: designing interfaces between components, choosing the right data models, planning for failure modes, and making trade-offs between complexity and capability.

Testing and Validation

If you didn’t write the code, testing becomes even more critical. Vibe coders should develop strong testing habits:

• Always ask the AI to generate tests alongside the implementation
• Write additional edge-case tests manually — the AI tends to test the happy path
• Run the code early and often; don’t trust that generated code works without verification
• Use type checking (TypeScript, mypy) as an automated safety net
• Set up CI/CD pipelines that catch issues before they reach production

Is Vibe Coding the Future of Software Development?

This is the question that sparks heated debates in every engineering team. Let me offer a balanced perspective based on what I’ve seen over the past year of working with these tools daily.

The Case For Vibe Coding

The trajectory is clear: AI coding tools are getting better at an accelerating rate. Features that required manual implementation a year ago are now one-prompt tasks. The gap between what you can describe and what the AI can build is narrowing with every model update. If this trend continues — and there’s no indication it won’t — vibe coding will handle an increasingly large percentage of software development work.

The productivity gains are real and measurable. In my own work, tasks that took hours now take minutes. Boilerplate that used to be copy-pasted and modified is now generated fresh, customized to the exact context. The barrier to building software is lower than it’s ever been, and that unlocks creativity for experienced developers while opening the door for newcomers.

The Case For Caution

But the hype outpaces reality in important ways. Vibe coding works well for the kind of code AI has seen before — standard web applications, common patterns, well-documented frameworks. When you move into novel territory — custom algorithms, unusual architectures, cutting-edge libraries — the AI’s output quality drops significantly.

There’s also a real risk of skill atrophy. Developers who vibe code exclusively may lose the ability to debug deeply, understand performance characteristics, or reason about system behavior from first principles. When the AI fails (and it will), you need the skills to take over manually. If you’ve outsourced all the “real coding” to an AI, that transition becomes painful.

The Balanced View

Vibe coding is a powerful tool in your toolkit, not a replacement for software engineering. The best developers in 2026 are those who know when to vibe code and when to write code by hand. They use AI to eliminate tedium, accelerate prototyping, and handle boilerplate — then apply their expertise to the parts that matter most: architecture, security, performance, and edge cases.

Think of it like the transition from assembly language to high-level languages. Nobody argues that we should go back to writing assembly for everything. But the developers who understand what happens at the machine level still have an edge when it comes to performance-critical code. Vibe coding is another step up the abstraction ladder — incredibly useful, but not a reason to forget what’s underneath.

Getting Started with Vibe Coding: A Practical Guide

Ready to try vibe coding for yourself? Here’s a concrete, step-by-step approach to getting started.

Step 1: Choose Your Tool

If you’re new to vibe coding, start with one of these paths:

• Terminal-first developers: Set up Claude Code — it’s the most powerful option for full-project vibe coding
• VS Code users: Try Cursor — the Composer feature is intuitive and the visual diffs make reviewing easier
• Minimal change: Start with GitHub Copilot’s chat and workspace features inside your existing VS Code setup

Step 2: Start with a Low-Stakes Project

Don’t try to vibe code your company’s production authentication system on day one. Start with:

• A personal side project you’ve been meaning to build
• An internal tool or script for your team
• A prototype or proof-of-concept for a new feature
• Refactoring tests — ask the AI to generate tests for existing code

Step 3: Write Detailed Prompts

The single biggest factor in vibe coding success is prompt quality. Follow these guidelines:

// Template for effective vibe coding prompts:

1. WHAT you want to build (feature/component/system)
2. CONTEXT about your existing codebase (frameworks, patterns, conventions)
3. REQUIREMENTS that are specific and testable
4. CONSTRAINTS (performance targets, compatibility needs, security requirements)
5. EXAMPLES of how it should behave (input/output examples, user flows)

// Real example:
"Create a caching layer for our API responses. Context: Express.js app,
TypeScript, Redis already configured in src/config/redis.ts. Requirements:
cache GET responses for 5 minutes by default, allow per-route cache
duration override via decorator, cache keys should include query params,
add cache-control headers to responses. Constraints: must not cache
authenticated responses (check for Authorization header), cache
invalidation on POST/PUT/DELETE to the same resource."

Step 4: Develop Your Review Workflow

After the AI generates code, follow this review checklist:

1. Architecture check: Does the generated code fit your project’s structure and patterns?
2. Security scan: Any hardcoded secrets, unsanitized inputs, or auth bypasses?
3. Dependency review: Did it add dependencies you don’t want? Are versions appropriate?
4. Error handling: Does it handle failure cases, not just the happy path?
5. Performance: Any obvious inefficiencies like N+1 queries or unnecessary loops?
6. Tests: Are the generated tests meaningful, or just placeholder assertions?
7. Run it: Actually execute the code. You’d be surprised how often “looks correct” and “actually works” diverge

Step 5: Iterate and Learn

Your first vibe coding sessions will be rough. The prompts won’t be specific enough, you’ll miss things in review, and the AI will generate code that doesn’t fit your project. That’s normal. Each session teaches you:

• What to specify upfront versus what to fix in iteration
• Which kinds of tasks the AI handles well versus poorly
• How to phrase requests to get code that matches your project’s conventions
• When to abandon the AI’s approach and write something manually

After a few weeks of regular practice, you’ll develop a feel for the workflow — and that’s when the productivity gains really kick in.

Common Vibe Coding Mistakes to Avoid

After a year of vibe coding across dozens of projects, here are the mistakes I see most often:

• Accepting code without reading it — Karpathy himself said he “barely looks at the code,” but he’s building personal projects. For production code, you must review every line. Blindly accepting AI output is a recipe for technical debt and security vulnerabilities
• Prompting too vaguely — “Build a login system” produces generic code. “Build JWT auth with refresh token rotation using our existing Prisma User model” produces useful code. Specificity is everything
• Not testing — Generated code that compiles is not the same as generated code that works. Run your tests. Add tests. Test manually. The AI is confident even when it’s wrong
• Over-relying on one tool — Different tools excel at different tasks. Claude Code is great for complex multi-file features. Copilot is great for in-line completions. Use the right tool for the right job
• Ignoring the learning opportunity — When the AI generates an approach you didn’t expect, take a moment to understand why. You might learn a better pattern — or identify a subtle bug the AI introduced
• Skipping version control — Commit before major vibe coding sessions and after each successful feature. If the AI makes a mess, you need to be able to roll back cleanly

The Bottom Line

Vibe coding is real, it’s useful, and it’s here to stay. It’s not the end of programming — it’s the evolution of it. The developers who will thrive in this new landscape are the ones who embrace AI as a powerful amplifier of their existing skills while maintaining the deep technical knowledge to handle what AI cannot.

Start small, iterate often, review carefully, and remember: the vibe is the direction, but your expertise is the compass. The AI can write the code. It’s your job to make sure it’s the right code.