What Is Vibe Coding? A Practical Guide for Developers
Vibe coding is a new way of working where developers describe what they want instead of writing every line of code by hand, and artificial intelligence turns that intent into working software. The term was popularized by Andrej Karpathy in early 2025 and quickly became part of the shared vocabulary around AI-assisted software development. Google Cloud describes it as a conversational workflow where the developer’s role shifts from writing code to guiding, refining, and debugging the AI tool. ([Google Cloud][1]) IBM puts it more simply: the practice of generating code by prompting AI tools instead of manually writing it. ([IBM][2])
Thinking of this approach as merely “asking AI to build an app” misses the point. Used well, vibe coding helps developers turn a product idea in their head into something tangible much faster. You describe a screen mockup, a small API endpoint, a data transformation script, or a test scenario; the tool gives you a first version. Then you run the result, notice what breaks, describe the behavior, and steer it again. The core skill here is not typing more code. It is explaining the system clearly, reading the output critically, and knowing when to pause and bring your engineering judgment back into the loop.
That is why vibe coding can look like a magical shortcut for beginners and a productivity booster for experienced developers. Both views are partly true. A beginner may spin up a prototype in a few prompt rounds that they could not have built alone before. An experienced developer may save serious time on boilerplate, sample tests, small refactors, documentation, or exploratory experiments. The difference shows up in what happens next: an experienced developer questions the generated code, thinks through edge cases, and opens up the security and maintenance implications. A beginner may assume everything is correct once it works on screen.
Vibe coding is not the same thing as traditional AI-powered code completion. With code completion, the developer is still firmly in the driver’s seat; the tool speeds up a line, function, or file. Vibe coding is more conversational. You might say, “List user roles in this panel, add search, show helpful text in the empty state, and display a retry button when the API fails.” The AI generates the code, you run it, and then continue with, “Search breaks with Turkish characters; make the filter locale-aware.” This workflow has become more visible as tools such as Cursor, Replit, GitHub Copilot, Claude Code, and similar assistants have grown more common. If you are new to Copilot, GitHub Copilot Beginner Guide: Set Up Your First Project is a useful companion read.
A good vibe coding session starts with clear context. Instead of saying “Build me a todo app,” you will get better results by defining the project frame, language, packages, target user, and acceptance criteria. For example: “In an existing dashboard using Next.js 15, TypeScript, and Tailwind, write a small task list component that runs only on the client side. It should support adding, completing, and deleting tasks. Keep state local. Use accessible button labels. Keep the code short and readable.” A prompt like this gives the AI both technical boundaries and quality expectations. From there, it is healthier to move in pieces rather than asking for a huge application in one shot: first the data model, then the component, then error states, then tests.
One of the most valuable habits in this workflow is asking the AI to “plan first, then implement,” instead of simply saying “do it.” Requesting the file structure and the intended changes first reduces accidents, especially in existing projects. In a large codebase, when you ask a tool to “add this behavior,” it may sometimes change the nearest-looking file, copy an older pattern, or move business logic into a layer it should never touch. The planning step gives the developer a chance to hit the brakes. You can correct the route early by saying, “This change belongs in the service layer; the component should only display state.”
Vibe coding is strongest in prototyping. Instead of spending weeks setting up infrastructure just to test whether an idea is worth pursuing, you can produce something clickable and testable in a few hours. Internal tools, demo screens, small automations, migration helpers, data-cleaning scripts, and personal workflow utilities are good candidates. That feeling was also central to Karpathy’s first description: the developer sees the result, talks to it, runs it, and talks again. Still, “it works as far as it appears to” is not the same as “it is reliable in production.” In areas such as payments, authentication, personal data, authorization, and public-facing APIs, vibe coding can only be the starting point.
The first major risk is readability. AI can often produce code that runs but is messy. Unnecessary abstraction, repeated blocks, shallow error handling, silently swallowed exceptions, and inconsistent naming are common. In a recent conversation reported by Business Insider, Karpathy also said AI-written code can still produce bloated and rough outputs that need improvement by human reviewers. ([Business Insider][3]) This does not make vibe coding bad; it makes code review more important. AI can behave like a fast intern, but you are still the person pressing the merge button.
The second risk is security. Without the right context, AI may fail to sanitize user input properly, leave authorization checks only in the interface, accidentally move secrets to the client side, or suggest outdated packages as dependencies. Cloudflare’s definition also emphasizes that vibe coding relies heavily on LLMs to generate code; heavy use brings a heavy need for review. ([Cloudflare][4]) That is why every vibe coding output should pass at least a basic security check: input validation, authentication boundaries, sensitive data in logs, dependency status, rate limiting, and error messages. Asking the AI to write tests is not enough on its own; you also need to check whether those tests cover meaningful scenarios.
A good developer practice is this: have the AI produce the smallest working piece first, then narrow it to your own standards. Ask it to explain what the code does, but do not accept the explanation as truth; compare it against the code. Then ask for tests, edge cases, performance weaknesses, and a security review. After that, walk through the files yourself. If you do not understand a function, that code is not really yours yet. If you want to gain speed with vibe coding, the rule “do not accept what you have not read” needs to sit at the center of your workflow.
When writing prompts, mixing product language with engineering language often works well. “The user should feel that something is happening after pressing the save button” is product language. “The button should be disabled in the pending state, aria-busy should be added, errors should show a toast, and form values should not disappear” is engineering language. When AI hears both at the same time, it is more likely to capture both the desired behavior and the implementation details. The same applies to debugging. Instead of saying only “it doesn’t work,” share the log, the expected behavior, the current behavior, and the last known working point before the change.
Tool choice matters too. Some developers are comfortable with assistants embedded inside the IDE, some prefer terminal-based agents, and others would rather discuss architecture in a web interface and move the code manually. Model differences, context window size, repository-reading ability, permission to run terminal commands, and privacy settings can significantly affect the result. If you are familiar with model comparisons for content creation, you can bring a similar mindset to coding; the evaluation logic in ChatGPT vs Gemini: Which Is Better for Content Creation? offers a useful frame for thinking about different models’ strengths and weaknesses.
Using vibe coding inside a team is different from experimenting on your own. If shared coding standards, review processes, commit messages, testing thresholds, and security checks are not clear, everyone brings their own AI habits into the repository. Over time, the codebase can turn into a fast-growing but hard-to-maintain mix of different styles. To prevent this, teams should clearly write down the rule that “AI-generated code goes through the same review process as normal code.” Adding prompt notes to pull request descriptions can also make it easier to show which decisions were made by a human and which parts came from the tool.
The best way to learn vibe coding is to choose a real, low-risk problem. A small tool you use yourself, a log-cleaning script, a JSON converter, a simple dashboard component, or a helper that generates test data can all be good starting points. In the first attempt, your goal should not be perfect code; it should be understanding the rhythm of the workflow. Prompt, run, share the error message, ask for a fix, read the code, simplify. After a few rounds, you will see more clearly where AI makes good guesses and where it stays superficial.
The heart of this developer guide is simple: vibe coding does not remove software development; it shifts the developer’s center of gravity. It requires less keyboard labor and more intent-setting, verification, architectural judgment, and quality control. Shutting the door on this style may be inefficient; accepting every output without question can create expensive mistakes. The strongest path is to use AI as a fast production partner without giving up ownership. If the code enters your product, your team still carries the responsibility.
