Vibe Coding is a way of building software by collaborating with AI models in real time. Instead of writing every line yourself, you guide the model through what you’re trying to achieve, then review, refine, and shape the output together. It’s fast, conversational, and ideal for exploring solutions without getting stuck in syntax or boilerplate.

The term was introduced by AI researcher Andrej Karpathy in early 2025, describing how developers were already starting to “vibe” with large language models. The idea is less about giving commands and more about building a shared context with the AI—almost like pair programming with a super-fast junior engineer who never gets tired.

Why It’s Gaining Traction

Many developers are turning to vibe coding because it makes the early stages of development smoother:

• You can prototype features quickly by letting the AI sketch out different approaches.
• It’s easier to brainstorm ideas without stopping to research syntax or API usage.
• You stay in flow by having the AI handle repetitive logic or boilerplate while you focus on structure.
• You can explore multiple implementations in less time than writing one by hand.

It’s not about shortcuts. It’s about getting to clarity faster.

How It Works in Practice

Here’s what vibe coding looks like in a real workflow:

Instead of prompting:

“Write a function to handle JWT validation.”

You might say:

“I’m building an Express.js route that needs to validate a user’s JWT before continuing. Can you help outline that flow?”

The AI gives you a general structure. You follow up with questions, tweak the logic, and adjust based on your stack. The interaction keeps going until the code feels right. The model helps build momentum, and you handle the decisions.

It’s less about precision and more about getting unstuck fast.

When Vibe Coding Makes Sense

Vibe coding isn’t the answer to every coding task, but it shines in specific scenarios—especially when speed, flexibility, and exploration are more important than perfection. Below are common situations where developers get the most value from this approach:

1. Starting new components or backend flows

When you’re building something from scratch, like a dashboard UI or a new API endpoint, the AI can help lay down the initial structure. You can describe the logic, and the model fills in the starting point—saving time on boilerplate so you can focus on real decisions.

2. Working with unfamiliar APIs or languages

If you’re switching from Python to Go, or exploring a third-party API for the first time, vibe coding acts like an in-session tutor. You can ask how to structure calls, handle responses, or work with unfamiliar syntax. The AI helps you ramp up faster without constantly switching to documentation.

3. Refactoring legacy code with unknown patterns

Old codebases often lack documentation, naming consistency, or clear structure. You can paste chunks of code into the AI and ask for summaries, alternative versions, or cleaner patterns. This makes it easier to navigate refactors without getting lost in the mess.

4. Building quick internal tools or MVPs

When speed matters more than perfect architecture, vibe coding helps you move fast. You can generate rough implementations, wireframe logic, or spin up entire CRUD interfaces quickly. It’s especially useful for hackathons, internal dashboards, or client demos.

5. Writing test coverage or setup scripts

Tests can be repetitive. You can ask the model to write unit tests, integration test scaffolds, or seed data generators based on your existing code. It can also help with things like ESLint configs, build pipelines, or deployment YAMLs. These are tasks where accuracy matters, but creativity doesn’t.

6. Working on early-stage product development

When the direction is still evolving, vibe coding lets you iterate fast without committing to a fixed structure too early. You can throw ideas at the model, explore different implementation paths, and change direction with minimal rewrite cost.

It’s about moving quickly and exploring ideas without getting bogged down. While it’s not a replacement for good engineering, it helps you get to version one faster so you can focus on what really needs polish.

What to Watch Out For

Vibe coding can be efficient, but it’s not foolproof. Here are the most common issues developers run into when collaborating with AI:

Hallucinations

The model may confidently generate code that looks real but has no basis in your stack or reality. Common hallucinations include:

• Nonexistent method names
• Fake third-party packages
• Fabricated type definitions or class properties
• Invented syntax (especially in edge cases or niche tools)

You’ll often catch these right away if you’re familiar with the language. But in unfamiliar frameworks, they can slip through and cause bugs later.

Incorrect suggestions

Even when the model uses real syntax, it may misapply it. For example:

• Using fetch() in a Node.js context without a polyfill
• Mixing React class components with functional components
• Calling lifecycle methods that no longer exist

Always test the result and sanity-check whether the logic applies to your environment.

Outdated code patterns

LLMs are trained on a wide range of internet code, much of which is outdated. You may see:

• Deprecated methods or lifecycle hooks
• Old JavaScript/TypeScript patterns (like callbacks instead of Promises)
• ESLint rule violations or formatting that’s inconsistent with your standards

Make sure to align the output with current best practices and your project’s style guide.

Security oversights

Security is one of the biggest risks in AI-assisted coding. Watch for:

• Directly injecting user input into SQL or shell commands
• Skipping CSRF/authorization checks in backend routes
• Hardcoding secrets or tokens in config files

If code touches user input, authentication, or external APIs, treat it as insecure until reviewed thoroughly.

Overengineering

LLMs often return verbose or overbuilt solutions. Examples include:

• Creating three files and two classes where a function would suffice
• Suggesting unnecessary use of Redux, Context, or state management libraries
• Adding abstraction layers that don’t serve a clear purpose

Lean code is usually better. Cut what you don’t need.

Shallow context understanding

AI doesn’t have full access to your repo or state unless you’re using an integrated coding assistant with live context (like Copilot in VS Code). So you might get:

• Variable or function name collisions
• Suggestions that don’t match your existing architecture
• Conflicts with naming conventions or folder structures

When in doubt, re-orient the AI with a reminder of what files, libraries, or patterns you’re using.

Final Thoughts

Vibe coding is changing how developers think and build. It blends human direction with AI speed, making it easier to go from idea to implementation. You still need to know what good code looks like, but the process feels less rigid and more exploratory.

If you’re building something new, under time pressure, or just want a faster way to prototype, this is worth trying.

You guide the vibe while the model keeps up.

You should learn more about how to work effectively with AI in your coding workflow. To help, here’s a post on 10 expert tips for coding with Claude AI.

Remember, you guide the vibe while the model keeps up.