What is Vibe Coding?

Vibe coding is one of the latest buzzwords in software development, describing an AI-driven approach where code is generated by simply describing what you want in plain English. Instead of a human writing and reviewing every line, the developer provides natural language prompts, and a large language model (LLM) such as ChatGPT or Google Gemini, acts as the “main developer” by producing the code. In other words, you tell the AI what it is you want, and it handles the heavy lifting.

How Vibe Coding Works

Unlike conventional AI-assisted coding (e.g. using GitHub Copilot or standard pair programming), vibe coding means fully trusting the AI’s output and iterating quickly, rather than carefully inspecting each snippet. Andrej Karpathy famously described it as “fully giving in to the vibes, embracing exponentials, and forgetting that the code even exists”. The human coder focuses on guiding the AI and testing the results, while largely ignoring the actual code under the hood.

A typical vibe coding workflow goes something like this:

Prompt the AI: Start by telling an AI-powered coding tool what you want (e.g. “Build a simple to-do list web app with a login screen”). The LLM then generates a chunk of code based the request.
Run and Observe: Rather than reviewing the code line-by-line, you run it and see what happens. If the app works (even roughly), great. If not, you note what’s wrong.
Iterate via Prompts: For any issues or new features, you give follow-up instructions to the AI instead of manual fixes. For example: “Fix the signup form validation error”. You feed error messages or requirements back into the prompt, and the AI adjusts the code accordingly.
Accept and Deploy: Once it “feels right,” you accept the AI-written code and deploy or use the application. The emphasis is on getting a working result, not on perfect code style or architecture.

Throughout this process, vibe coders treat the AI like a very enthusiastic junior developer – one who can produce code rapidly, albeit with quirky solutions. The developer’s role becomes more about describing goals and validating the outcomes, rather than hand-crafting the code. This can feel like coding on cruise-control, trading strict precision for speed and creativity.

Benefits and Use Cases of Vibe Coding

Proponents of vibe coding highlight several benefits and ideal scenarios for this approach:

Rapid Prototyping: Vibe coding shines for quickly spinning up prototypes. You can transform an idea into a functional interface in a short time by describing features. For example, a basic app that might take days to code from scratch can be vibe-coded in a matter of hours.
Accessibility and Democratisation: By letting AI handle the complex syntax, vibe coding lowers the barrier to entry for programming. Even people with minimal coding experience can create working software by simply “talking” to the AI.
Speed and Productivity: When used appropriately, vibe coding can turn tedious hours of writing boilerplate code into minutes. The AI quickly generates routine components (forms, UI elements, basic functions), freeing developers to focus on high-level problem solving. Studies of new startups showed a significant shift toward AI-generated code.
Creative Experimentation: Because you’re not investing time in every line of code, it’s easier to experiment and iterate on ideas. Developers can try out wild or unconventional features without fearing wasted effort if it doesn’t pan out.

Challenges and Drawbacks

Vibe coding does come with significant caveats. Here are some of the key challenges and downsides to be aware of:

Code Quality & Maintainability: Because the AI-generated code isn’t being meticulously reviewed, the resulting code can be messy or suboptimal. Best practices, clean architecture, and documentation often fall by the wayside. This makes vibe-coded projects hard to maintain or scale.
Bugs and Security Risks: Large language models sometimes “hallucinate” producing code that is incorrect or insecure. If you’re not reading through the code, you might miss subtle bugs or vulnerabilities. For example, an LLM might use an outdated API or skip input validation, leading to potential security holes.
Dependency and Skill Atrophy: Vibe coding makes you heavily dependent on the AI and the specific tools used. If the AI model updates or an API changes, your generated code could break, and without a solid understanding you might be stuck.

In short, vibe coding trades off some reliability and clarity for speed and convenience. It’s best approached with eyes open to these risks. Many experts recommend using it for what it’s good at (quick solutions, throwaway projects, learning new APIs) while avoiding it for mission-critical software.

What is a “Vibe Coding Cleanup Specialist”

One of the more ironic outcomes of the vibe coding trend is the emergence of a new job role: the “vibe coding cleanup specialist.” In recent months, some developers on LinkedIn have even begun adding this title to their profiles. These are experienced programmers who offer to swoop in and fix code that was generated via vibe coding once the original creators hit a wall.

Essentially, when a non-technical user vibe-codes an app and ends up with buggy, tangled code that the AI can’t fix on its own, a cleanup specialist can either prompt the AI more effectively to resolve the issues or manually repair the code base.

This highlights an interesting cycle: AI tools empower people with little coding knowledge to build applications quickly, but when those applications break or need polishing, human experts are called in to clean up the mess.

The rise of the vibe coding cleanup specialist (much like the short-lived craze for “prompt engineers”) is a reminder that even in an AI-assisted future, skilled developers aren’t going away, their role is just shifting.

Conclusion

Vibe coding represents a bold new way to create software by collaborating with AI. It can make programming feel more like “directing” and less like grinding through syntax, offering huge boosts in productivity and creative freedom for the right projects.

At the same time, it’s not a silver bullet. The approach is best used for prototyping, personal projects, or learning, where speed matters more than polish. For large-scale, critical software, the traditional virtues of clean code, thorough testing, and sound engineering still reign supreme.