What Is Vibe Coding? The Developer's Guide

Andrej Karpathy coined the term “vibe coding” in February 2025. In a post on X, the former OpenAI researcher and Tesla AI director described a workflow where he “fully gives in to the vibes,” describing what he wants in natural language and letting AI generate the code. He sees stuff, says stuff, runs stuff, and copy-pastes stuff. It mostly works. The term went viral, resonated across the industry, and became Collins Dictionary’s Word of the Year for 2025.

But what does vibe coding actually mean in practice? Where does it help, where does it fail, and how do you do it well?

What Vibe Coding Looks Like

Vibe coding is not magic. It’s a shift in the medium of expression. Instead of typing code character by character, you describe intent in natural language. The AI (ChatGPT, Claude, Cursor, Copilot, or similar) generates code. You review the output, run it, and iterate conversationally until it does what you need.

A typical session: “Add a function that fetches user data from the API and caches it for 5 minutes.” The model returns a function. You run it. It works for the happy path but fails on network errors. You say, “Add retry logic with exponential backoff.” The model updates the code. You test again. You iterate.

The feedback loop is conversational. You don’t debug by reading stack traces and tracing execution. You describe the problem: “It’s throwing a 429 when the rate limit is hit.” The model suggests a fix. You apply it, test, and move on. The speed comes from skipping the manual translation of intent into syntax. The risk comes from skipping the deep understanding that manual translation forces.

The key difference from traditional coding: you’re steering with language, not writing every line yourself. You’re still making decisions. You’re still responsible for what ships. The tool changes how you express those decisions, not whether you make them.

Where It Works Well

Vibe coding excels at tasks with clear patterns and well-defined scope.

CRUD operations. Create, read, update, delete. The model has seen thousands of examples. Describe the schema and endpoints, get working code. Boring, repetitive, and exactly what AI handles well.

Boilerplate and scaffolding. Project setup, config files, standard folder structures. “Create a Next.js app with TypeScript, Tailwind, and a blog layout.” The model knows the patterns. You get a starting point in seconds instead of minutes.

Prototyping. Throw together a proof of concept. Validate an idea before investing in production-quality implementation. Speed matters more than perfection.

Test generation. “Write unit tests for this function.” The model sees the code, infers the behavior, and generates test cases. You still need to review and run them, but the first draft is fast. Same for documentation, refactoring repetitive blocks, and translating between languages or frameworks.

For these tasks, the AI coding workflow that actually works is straightforward: give the model context, state constraints, and review every line before you ship.

Where It Fails

Vibe coding is not a replacement for judgment. It breaks down in several categories.

Safety-critical systems. Medical devices, financial transactions, aviation software. The stakes are too high to deploy code you don’t fully understand. AI generates plausible code. Plausible is not correct. When failure means harm, you need to own every line.

Performance-sensitive code. The model optimizes for readability and common patterns, not for your specific bottleneck. It doesn’t know your latency budget, your memory constraints, or your scale. Hot paths, tight loops, and resource-constrained environments need human expertise.

Complex architectural decisions. “Should we use microservices or a monolith?” The model will give you an answer. It won’t know your team size, your deployment constraints, or your org’s politics. Architecture is context-dependent. AI has no context beyond what you provide.

Novel or domain-specific logic. If the pattern isn’t in the training data, the model will guess. It might guess well. It might not. When you’re doing something the model hasn’t seen before, you’re on your own.

The boundary isn’t fixed. A task that was novel two years ago might be routine now as models improve and training data grows. The skill is knowing when you’re in familiar territory and when you’re not.

The Adoption Reality

Vibe coding is not a niche practice. According to JetBrains’ 2026 Developer Ecosystem Survey, 92% of US-based developers use AI coding tools as part of their daily workflow. The term itself entered the mainstream: Collins Dictionary named “vibe coding” its Word of the Year for 2025, defining it as programming by describing intent in natural language rather than writing code manually.

The shift is real. What started as a viral tweet is now standard practice. The question is how to use it without the pitfalls.

Common Pitfalls

Deploying without understanding. Surveys show that 40% of junior developers have deployed AI-generated code into production. The code might work. It might also contain subtle bugs, security holes, or design flaws you can’t spot because you didn’t write it. If you can’t explain the code to a colleague, don’t ship it. You’ll own it when it breaks.

Debugging takes longer than writing. In the 2025 Stack Overflow Developer Survey, 45% of developers reported that debugging AI-generated code is more time-consuming than writing code manually. Another 66% said AI solutions are “almost right, but not quite.” The model gives you a head start. It also gives you code you didn’t design, with assumptions you didn’t make. Fixing “almost right” can eat the time you saved.

Over-relying on the first output. The model is statistically plausible, not correct. It will confidently produce code that looks right and isn’t. Understanding how LLMs work helps: they predict tokens, they don’t reason. Treat every output as a draft. The model has no memory of your codebase conventions, no awareness of your deployment environment, and no way to know when it’s wrong. You’re the one who has to catch that.

How to Vibe Code Well

Treat it as collaboration, not delegation. You’re the architect. The model is a fast first-draft writer. You provide direction, constraints, and context. You decide what to keep and what to throw away.

Verify everything. Run the code. Test edge cases. Check dependencies. Read every line, especially the parts you didn’t ask for. The model might “helpfully” add error handling that swallows exceptions or a dependency you don’t need. Skimming is how bugs slip through.

Understand the fundamentals. The best vibe coders are people who could write the code themselves. They know when the output is wrong. They know when to iterate and when to rewrite. Prompt engineering helps you steer the model, but it doesn’t replace knowing your craft.

Know when to stop. If you’re on the third revision and it’s still wrong, rewrite. Sometimes the best move is to delete everything and start over with a clearer problem statement. Iteration has diminishing returns. At some point, the accumulated patches create more confusion than clarity.

Match the task to the tool. Use vibe coding for the tasks where it shines. Use your own judgment for the rest. The developers who struggle are the ones who try to vibe code everything, including the parts that need human expertise.

Not No-Code, Not Low-Code

Vibe coding is often confused with no-code or low-code tools. It’s different. No-code platforms hide the code behind drag-and-drop interfaces. Low-code reduces the amount of code you write. Vibe coding still produces real code. You’re still a developer. You’re still reading, reviewing, and modifying code. The shift is in the medium of expression: you describe intent in language, the model generates the implementation, and you refine it.

It’s a new way to write code, not a way to avoid writing it.

The people who get the most from vibe coding are the ones who treat it as a lever. They use it to go faster on the right tasks. They verify the output. They understand the fundamentals. They know when to trust it and when to take over. The goal isn’t to replace your judgment. It’s to amplify it. Get Insanely Good at AI covers these mechanics in depth: how AI coding tools work, why they fail in the ways they do, and how to build workflows that actually ship.