What Is Vibe Coding?

The term "vibe coding" was coined by Andrej Karpathy in early 2025 to describe a radical shift in how software gets written. Instead of typing code character by character, you describe what you want in natural language and an AI agent writes all the implementation. You're the architect and reviewer; the AI is the builder.

When Karpathy first described it, many senior engineers dismissed it as a gimmick. A year later, it's how a significant portion of production code gets written — including at companies you'd recognize. The question is no longer "should you vibe code?" but "how do you vibe code without shipping unmaintainable garbage?"

Why It Actually Works in 2026

Three things changed since the early days of AI coding:

1. Models got genuinely good at code. Claude Sonnet 4.6, GPT-4o, and Gemini 2.5 Pro don't just autocomplete — they understand architecture, patterns, and conventions. They can read a 10,000-line codebase and produce code that looks like a human wrote it, following the same style throughout.
2. Agentic tools close the loop. Tools like Claude Code don't just generate code — they read existing files, run the code, see errors, fix them, run tests, and iterate. The human doesn't need to copy-paste and debug anymore.
3. Context windows are enormous. With 200K+ token context windows, the AI can hold your entire project in memory simultaneously. It knows about that utility function in lib/utils.ts, that type definition in types/index.ts, and that edge case handler in middleware.ts.

The Professional Vibe Coding Workflow

Vibe coding done poorly produces fragile, inconsistent code that nobody can maintain. Vibe coding done well produces code indistinguishable from hand-written work in a fraction of the time. Here's the difference:

Step 1: Write a Comprehensive CLAUDE.md

Before your first prompt, establish a contract with the AI. The CLAUDE.md file (or equivalent for other tools) is the project brief that ensures consistency across every prompt:

# CLAUDE.md

## Project
Portfolio website + admin dashboard for a web agency.

## Tech Stack
- Next.js 15 (App Router), TypeScript strict, Tailwind CSS v4
- MongoDB with Mongoose, Zod validation
- Resend for email, Vercel for hosting

## Architecture
- Server Components by default
- "use client" ONLY for components that need hooks or event handlers
- API routes: app/api/[resource]/route.ts
- DB operations: lib/db/models/ (Mongoose) + lib/db/connect.ts (singleton)
- All forms use Server Actions, not API routes

## Conventions
- File naming: PascalCase for components, camelCase for utilities
- All inputs validated with Zod before touching the database
- Error boundaries on every async Server Component
- Conventional Commits for all git messages

## Never Do
- Use `any` — always provide types
- Leave console.log in production code
- Use inline styles (use Tailwind)
- Install new dependencies without asking first
- Create files that duplicate existing functionality

This single file is the difference between getting consistent, maintainable output versus random code that fights your existing patterns.

Step 2: Describe Outcomes, Not Instructions

The #1 mistake new vibe coders make is writing implementation instructions instead of describing desired outcomes. The AI is better at choosing implementation details than you think — but it needs to understand the why.

Bad Prompt	Good Prompt
"Create a file UserCard.tsx with props name, email, avatar as strings, render them in a div with Tailwind classes"	"Add a user card to the team section that shows avatar, name, and role. Match the existing card style from ProjectCard.tsx. Should link to the user's profile page."
"Write a function that takes an array and returns unique values"	"The tag filter on the blog page shows duplicates. Fix it so each tag appears only once, sorted alphabetically."
"Add a try-catch around the database call"	"The projects page crashes when the DB is slow. Add proper error handling — show a retry button to the user, log the error for debugging, and don't break the rest of the page."

When you describe the outcome, the AI makes better architectural choices because it understands the context — the existing patterns, the user experience goal, and the constraints.

Step 3: Review Every Diff — Seriously

This is non-negotiable. Vibe coding is not "AI writes, human ships." It's "AI writes, human reviews and approves." Read every diff the same way you'd review a PR from a junior developer. Common things AI gets wrong:

• Over-engineering: Creates abstractions for one-time operations. Three simple lines become a utility function, a type, and an interface.
• Stale patterns: Uses deprecated APIs from its training data (e.g., getServerSideProps in a Next.js 15 App Router project).
• Missing edge cases: Happy path works perfectly. The "user submits an empty form" path is forgotten.
• Security assumptions: Trusts client input, skips CSRF protection, uses weak comparison operators.
• Silent failures: Catches errors and swallows them instead of surfacing them to the user.

Step 4: The Feedback Loop Is Everything

The real speed comes from rapid iteration. Each turn is seconds, not minutes:

"Make the error message friendlier and add a retry button."
"The loading state flickers — add a minimum display time of 500ms."
"Good, but the mobile layout breaks at 375px. Fix the grid."
"Perfect. Now add a test for the error case."

Each of these turns takes 10-30 seconds for the AI to implement. Manually, each would take 5-15 minutes of writing, testing, and debugging. That's the real 10x — not one-shot generation, but rapid iteration.

What Vibe Coding Is Great For

• CRUD operations: Forms, tables, API routes, database queries — the bread and butter of web development. AI writes these flawlessly because it's seen millions of examples.
• UI implementation: Converting a design to components with Tailwind CSS. AI is remarkably good at translating visual descriptions into pixel-perfect markup.
• Boilerplate and scaffolding: Setting up authentication flows, email templates, admin panels, configuration files.
• Refactoring: Renaming across files, migrating from one library to another, converting class components to hooks.
• Test writing: Describe the behavior, AI writes the test cases. It often catches edge cases humans miss.

What Vibe Coding Is NOT Good For

• Security-critical code: Authentication, authorization, payment processing, encryption — always audit manually, line by line.
• Novel algorithms: If the problem hasn't been solved before in the AI's training data, it will hallucinate a plausible-looking but incorrect solution.
• Performance-critical hot paths: The AI will produce correct but not optimal code. Profile first, then optimize manually.
• Architecture decisions: The AI will happily build whatever you ask. It won't tell you that your approach is fundamentally wrong or that a simpler solution exists. Architecture is still a human job.
• Business logic: The AI doesn't know your domain. It can implement "discount 20% for orders over $100" but can't tell you if that discount strategy makes business sense.

The Uncomfortable Truth

Vibe coding works brilliantly for experienced developers who know what good code looks like and can spot bad code instantly. It's dangerous for beginners who can't tell if the AI's output is correct, secure, or maintainable. The irony: the people who benefit most from vibe coding are the ones who could write the code themselves — they just do it 10x faster now.

If you're learning to code, use AI as a tutor, not a ghostwriter. Understand why the code works before shipping it. The developers who thrive in 2026 are the ones who can both vibe code for speed AND hand-write code when precision matters.