What is vibe coding?
Author: Domeny.ForSale editorial team Published
In brief: Vibe coding is a way of creating software in which you describe the desired outcome to artificial intelligence, and it generates and revises the code. In its strict sense, it means working mainly through conversation and assessing how the application behaves, without closely examining its code. It makes building prototypes easier, but a working screen is not proof that a program is secure, correct, and ready for customers.
How vibe coding works
Instead of writing each instruction in the program yourself, you give an AI tool an instruction in ordinary language, known as a prompt. You might ask: “Build a simple service quote calculator, with a choice of package and number of hours.” The tool proposes code and, in some environments, also creates files, runs the application, and displays a preview.
You then check the result and describe changes: “Add a reset button,” “The form extends beyond the screen on a phone,” or “The price cannot be negative.” The AI modifies the solution, and you test it again. The main way you direct the work is by discussing the outcome, not by manually editing code.
The term is sometimes used more broadly to describe almost any programming with AI. However, it is worth maintaining a distinction: the essence of vibe coding is not simply having an assistant, but accepting its changes primarily on the basis of what the application does and how it looks.
How it differs from AI-assisted programming
A programmer using AI can ask it to write a function, then read it, check its assumptions, and deliberately incorporate it into the project. In vibe coding, that stage may be limited or skipped: the user focuses on the result and asks the model to fix successive problems.
| Way of working | Basis for accepting changes | Main limitation |
|---|---|---|
| Vibe coding | Application preview and attempts to use it | Problems not visible on screen are easy to overlook |
| AI-assisted programming | Code review, tests, and understanding the solution | Requires technical knowledge and time for verification |
The boundary is not rigid. A project may begin with open-ended experimentation and later move into structured engineering work. The important thing is not to confuse a quick prototype with a finished product.
Vibe coding can be accessible to someone who does not know a programming language. However, lacking that knowledge makes it harder to assess why an application fails and whether a proposed fix actually addresses the cause of the problem.
Which tasks it suits best
A sensible use is small experiments where errors have limited consequences: an interactive website mock-up, a demonstration of an idea, a calculator using fictional data, or a tool for a one-off transformation of a copy of a file. Projects like these let you quickly check whether an idea is useful.
For example, the owner of a service business wants to test a quote form. They can ask AI for a prototype with three packages and defined calculation rules, then show it to colleagues. At this stage, the form does not need to accept payments, store customer data, or send quotes.
The situation changes when the calculator is meant to handle real orders. You then need to verify, among other things, the calculations, data protection, and whether users can alter the price sent to the system themselves. This is no longer just a matter of how the interface looks.
A practical rule: the greater the consequences of a mistake, the less sensible it is to rely solely on conversation with AI and manually clicking through the application. Systems that handle payments, sensitive data, or user permissions require appropriate technical oversight.
What risks lie behind a working preview
AI can generate code that looks convincing but contains errors. An application may work with sample data but fail when faced with an empty field, an unusual character, or two simultaneous operations. Fixing one problem can also break another feature.
Security is a separate risk. An API key, a secret that enables access to an external service, should not end up in a website’s public code. Simply hiding a button does not secure an operation, and a login screen does not prove that the application properly restricts access to data.
There is also project maintenance to consider. If you do not understand how the project is built, it is harder to assess the effects of updates to libraries—the ready-made components used by the program. Successive fixes may increase complexity rather than address the source of the trouble.
Before using a particular tool, check its official documentation for the rules governing the processing of code and data you submit, the scope of its access to files, and the costs of running the application. Do not assume that generating a project includes keeping it running indefinitely for free.
How to build your first prototype safely
To start, choose a task whose result you can check yourself. For a quote calculator, the procedure could be as follows:
- Write down the rules before generating anything. Define prices, permitted values, and how the result should be rounded.
- Limit the scope. Ask for a version without login, payments, or storage of personal data.
- Make small changes. After each one, also check the features that were already working.
- Test the boundaries. Try zero, a negative number, an empty field, and a very large value. Compare the results with manual calculations.
- Keep working versions. Use version history or copies of the project so that you can undo an incorrect modification.
Before making the prototype available to customers, have it reviewed by someone who can assess the code and how the application is run. If you are only testing an idea, start with one feature using fictional data—without connecting business accounts or systems.
See also: What is an LLM?
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