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World desk7 min

What Is Vibe Coding, and How Does It Work?

Vibe coding turns software development into a prompt-and-feedback loop—but the term can also mean accepting code without understanding it. Here’s how the workflow works and where review matters.

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Vibe coding is a way of building software by telling an AI coding tool what you want, running the code it creates, and guiding changes through follow-up prompts. The term is also used more narrowly for a hands-off approach in which someone accepts generated code without reviewing or understanding it. That distinction matters: using AI to help write code does not have to mean trusting it blindly.

What vibe coding means

In everyday use, vibe coding describes a conversational loop: a person explains an intended result in ordinary language, an AI tool produces or changes software, and the person reacts to what happens. Instead of writing every line directly, the user steers the work by describing goals, constraints, errors, and desired revisions.

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The term has a narrower meaning in the OpenSSF glossary: accepting AI-generated code without reviewing or understanding it, then relying on the results and additional prompts. Under that definition, the defining feature is not simply that AI wrote code. It is that the person lets the code stand without examining what it does.

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That difference resolves a common ambiguity. Someone who asks an AI to draft a function, inspects the result, tests it, and edits it is using AI-assisted programming, but not necessarily vibe coding in the narrower sense. Someone who repeatedly accepts generated changes because the application appears to work is closer to that narrower usage.

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Search coverage commonly credits Andrej Karpathy with introducing the phrase in February 2025. That attribution and date are secondary-source reporting; the original post has not been independently confirmed here. The phrase is now used more broadly than its narrower original sense, so it helps to say whether you mean conversational AI programming generally or accepting code without understanding it.

How the vibe-coding loop works

The process is iterative rather than a single request that reliably produces a finished application. Each pass gives the tool more direction based on what you want or what you observed.

  1. Describe the outcome. Explain what the application should do, who it is for, and how it should behave. Include relevant constraints, such as what information it should collect or what should happen after a user takes an action.
  2. Ask the AI to create or change code. Depending on the tool, it may return code for you to use, or it may work directly with a project. Give it enough context to understand the change you want.
  3. Run the result. Try the application or feature rather than assuming that generated code works because it looks plausible. Follow the main path a user would take.
  4. Observe what happens. Note what works, what fails, and what differs from your intended behavior. A specific observation gives you something actionable to report.
  5. Prompt again or take manual control. Describe the problem or desired change, then run and evaluate the updated result. If the change is important or the AI is not resolving the issue reliably, inspect or edit the code directly.

For example, a person making a small task tracker might ask for a page where they can add tasks and mark them complete. After running it, they might find that refreshing the page clears the list, or that the completion control is confusing. They can report the observed behavior and ask for a targeted change. This example illustrates the prompt–generate–run–observe–revise loop; it does not guarantee that any particular tool will build or store tasks correctly.

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What the AI tool may be able to do

“AI coding tool” can mean different levels of access. A conversational assistant may suggest code for a person to review and apply. A coding agent may be able to inspect a project, edit its files, and run shell commands. Those capabilities change both the speed of the workflow and the consequences of a mistaken instruction.

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Mistral’s documentation describes Vibe Code as a supervised tool with filesystem read/write access and a shell. It can inspect files, run commands, write code, and open pull requests under user supervision. This is one documented example, not a description of every product called a vibe-coding tool.

Before starting, establish what your chosen tool can access and do. In particular, find out whether it can only generate suggestions or can also change project files and execute commands. Where it can act on a project, understand how it presents changes for review and what user supervision its workflow expects.

How to give the tool useful direction

Natural-language instructions are the steering mechanism, but a vague request leaves the tool to fill in important details. You do not need to write a formal software specification for every experiment. You do need to state the intended behavior clearly enough to judge the result.

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  • State the user-facing goal. Say what a person should be able to do, not just that you want a “modern” or “better” app.
  • Describe the expected behavior. Explain what should happen before and after an action, including what should happen when information is missing or an action fails.
  • Set boundaries. Name relevant limits, such as which part of the project should change or which existing behavior should remain intact.
  • Ask for a focused change. When reporting a problem, state what you did and what happened. Separate unrelated requests so you can tell which change fixed or introduced a behavior.
  • Check the result against the request. Test the expected path and at least consider what could happen outside it. A successful demonstration of one path does not establish that every case works.

These habits improve the feedback loop because they make intent and evaluation explicit. They do not make an AI-generated answer correct by themselves; the result still has to be checked.

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Why people use vibe coding—and what it does not remove

Vibe coding can make it easier to start a prototype because the first step can be describing an idea instead of manually writing all of its code. It can also make revision conversational: a user can explain a visible problem and request a change without first knowing how to implement that change line by line.

But less direct code writing does not mean less responsibility for the outcome. A scholarly analysis of the practice describes effort shifting toward supplying context and evaluating what the AI produces. A person still has to decide whether the result matches the goal, whether a change broke something else, and whether the code is appropriate to rely on.

Fluent explanations and a working-looking screen are not proof that software is correct or secure. Martin Fowler has identified security as a serious concern with unreviewed AI-generated code. The specific danger in the narrower form of vibe coding is accepting code without understanding or reviewing it. No quantified estimate of how often this leads to vulnerabilities is established here, so a precise risk rate should not be inferred.

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Choose a workflow to match the project

The right amount of review depends partly on what you intend to do with the result. A disposable experiment and software that people will rely on over time do not call for the same level of confidence. The core decision is whether you are exploring an idea or taking responsibility for a maintained application.

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For an experiment or prototype

It can be reasonable to prioritize speed while exploring whether an idea is useful. Still, run the result and check the central interaction rather than treating generated code as proof of a working prototype. Be clear about the limits of the experiment, especially if another person might mistake a demonstration for a dependable service.

For software you will keep using or maintain

Review consequential changes before relying on them. Pay attention to code that handles important data, access, or actions, and test behavior beyond the one path shown in a quick demonstration. If you cannot tell what a change does or how to assess it, do not treat a successful prompt exchange as a substitute for that understanding.

When comparing tools, focus on practical workflow rather than labels. Ask whether the tool only proposes code or can act on the project, whether file and shell actions are supervised, how you can inspect changes, and whether the tool fits an experiment or ongoing maintenance. The available descriptions support these comparison questions, but do not establish a full independent ranking of products.

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Common pitfalls and how to respond

The result looks right but has not been evaluated

A generated interface can appear convincing without demonstrating that its behavior is correct. Run the main workflow yourself and compare what happens with what you requested. If it fails, report the concrete steps and observed result, then test the revision.

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The prompt leaves key behavior unstated

If you describe only a broad outcome, the tool may make assumptions about details you care about. Add the missing expected behavior and constraints, then check that the new version preserves the parts that were already working.

The tool changes files or runs commands you did not expect

First establish the tool’s permissions and how it shows its actions. For an agent with project access, examine changes before treating them as acceptable. If you cannot understand the scope of an action or the resulting code, pause rather than continuing to approve changes by appearance alone.

Repeated prompts are not resolving the problem

More prompting is not always the best next step. Restate the desired behavior and the observed failure more precisely; if the issue remains consequential or opaque, inspect the code or ask someone qualified to review it. The workflow can shift from prompting to direct evaluation and manual intervention.

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A practical standard for using the term

Use “vibe coding” in its broad sense when discussing a conversational way to direct AI-generated code, but clarify that it can also mean accepting code without understanding or reviewing it. That narrower version is the one that raises the sharpest concern: the user may see an immediate result while missing defects in what was generated.

Whatever label you use, the useful cycle is to state intent, generate or change code, run it, observe the result, and decide whether to prompt again or inspect the code yourself. The more consequential the software, the less reasonable it is to let an apparently successful result stand in for evaluation.

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