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Vibe coding is a way to build software by describing what you want in ordinary language, asking an AI coding tool to create or change code, running the result, and steering revisions through follow-up prompts. The phrase is also used more narrowly for a hands-off approach in which someone accepts AI-generated code without reviewing or understanding it. Those meanings overlap, but they are not identical: using AI to help write software can include careful human review, while vibe coding in the narrower sense relies more heavily on the output simply working.

What vibe coding means

In a typical vibe-coding session, a person starts with an outcome rather than a detailed implementation plan. They might describe a small app, a page, or a feature, then ask an AI coding tool to produce the code. The person tries the result, reports what is wrong or what should change, and repeats the cycle.

The important distinction is how the person treats the generated code. In broad everyday use, “vibe coding” can mean conversational, AI-assisted programming. In the narrower definition recorded by the OpenSSF glossary, it means accepting generated code without reviewing or understanding it, then relying on observed results and further prompts. Someone who uses an AI assistant but reads, tests, and edits its code is still doing AI-assisted programming, but not necessarily vibe coding in that stricter sense.

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The phrase is commonly credited in secondary coverage to Andrej Karpathy in February 2025. That attribution and date have not been independently confirmed against his original post here, so they are best treated as reported origin rather than a verified publication date. The wording has since been used for a range of workflows, from guided experimentation to almost entirely hands-off code generation.

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How the vibe-coding loop works

Vibe coding is not a single prompt that reliably produces finished software. It is an iterative loop: state an intention, get a change, run it, inspect the result, and refine the request. The user’s effort shifts from typing every line toward explaining context, evaluating behavior, and deciding when to take manual control.

  1. Describe the outcome. State what the application or feature should do and how it should behave. A request such as “make a simple task list” leaves many decisions open; a more useful description says what users can add, edit, complete, or remove, and what should happen when the list is empty.
  2. Ask the tool to generate or change code. A conversational assistant may return code for you to copy or adapt. A coding agent may be able to inspect a project, write files, and run commands. The tool’s permissions determine whether it merely suggests changes or can act directly on your project.
  3. Run the result. Try the application or feature in the way a user would. A response that looks plausible in a chat is not proof that the code runs or behaves as requested.
  4. Observe what happens. Note the specific behavior that works, fails, or differs from your intention. A concrete report—such as “the saved item disappears after I reload”—gives the tool more useful direction than “it is broken.”
  5. Ask for a focused revision. Describe the observed problem or the next change. Then run the updated result and evaluate it again. Several rounds may be needed because an edit can fix one behavior while affecting another.
  6. Choose whether to inspect or edit the code yourself. For a disposable experiment, you may accept more uncertainty. For code you plan to rely on, review and test consequential changes instead of treating successful-looking output as sufficient.

What the AI tool may be able to do

“AI coding tool” covers different levels of access. Some tools generate code in a conversation and leave the user to place and run it. Others work as agents inside a project, where they can inspect files, make changes, and execute commands. Do not assume every product has the same permissions, review process, or ability to act without your intervention.

Mistral’s documentation describes Vibe Code as an example of the more agentic model: it can read and write files, use a shell, inspect a project, run commands, write code, and open pull requests under user supervision. That description applies to this documented tool, not to all products called vibe-coding tools. Before using any agent, establish what it can access and what actions require your supervision.

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Questions to ask before choosing a workflow

  • Does it only suggest code, or can it change your project? Suggestions require you to apply them; project access means the agent may modify files directly.
  • Can it run shell commands? If so, find out when commands are run and whether you approve them. A shell gives an agent a different level of ability from a chat-only code generator.
  • Can you see and review its changes? A visible change set gives you a chance to inspect what was modified before relying on it.
  • What is the project for? A throwaway prototype and software meant to be maintained have different consequences if the code is incomplete or misunderstood.

These questions are more useful than assuming that a particular label guarantees a particular workflow. Tool capabilities and supervision requirements vary; the available descriptions do not establish a comprehensive independent comparison of vendors.

What makes a prompt useful

A prompt is a way to communicate intent, not a substitute for deciding what the software should do. Give the tool enough context to distinguish the desired behavior from plausible alternatives. For a small feature, useful details include who will use it, what inputs it accepts, what should happen in ordinary cases, and what result counts as success.

For example, rather than asking only for a “task list,” specify whether a task can be marked complete, whether completed items remain visible, and whether data should still be there after the page is refreshed. These details make the request easier to evaluate: you can check the resulting behavior against the requirements you gave. If the first version misses something, report the actual mismatch and request a targeted change.

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Keep track of decisions as the project grows. A tool may not know why an earlier choice was made, and a follow-up prompt can change behavior that you wanted to preserve. Restating relevant context and checking the result after each meaningful revision can help keep the work aligned with your intent.

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How to tell whether the result is good enough

AI-generated code can look convincing and still be incorrect, incomplete, or insecure. Fluent explanations and a successful first run are not guarantees. The OpenSSF glossary’s narrower definition makes the central concern explicit: if you accept code without understanding or reviewing it, defects can remain hidden. Martin Fowler has also identified security as a serious concern, but no verified, named statistic establishes how frequently vibe-coded software contains vulnerabilities.

Use the level of review that matches what you intend to do with the result. For an experiment, you may be willing to tolerate breakage or discard the project. Before relying on software beyond an experiment, test the important behaviors and review consequential code. In particular, do not treat a working demonstration as proof that the implementation is safe or ready for ongoing use.

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A practical review checklist

  • Check the requested behavior: try the main actions you described and confirm the outcomes match your expectations.
  • Try more than the happy path: consider missing, unusual, or invalid inputs and see whether the software handles them sensibly.
  • Recheck after revisions: a change that addresses one issue can affect another part of the project.
  • Review changes that matter: understand the effect of modifications before depending on them, especially if the tool can edit project files or run commands.
  • Decide whether you can maintain it: if you cannot tell what the software does or how to correct it when it fails, that is a reason to seek a more thorough review before relying on it.

This checklist is not a guarantee of correctness or security. Its purpose is to keep evaluation in the loop rather than letting code generation stand in for it.

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When vibe coding is a good fit—and when it is not

The workflow can make it easier to get started when you have a clear idea but do not want to begin by writing every line yourself. It can also support quick experimentation: generate an initial version, see whether it fits the idea, and refine it in conversation. The main advantage is a different way to express and iterate on intent—not a guarantee that the tool will produce finished software.

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The trade-off becomes more important as the consequences of failure increase. If you are building something you will maintain or rely on, less direct code writing does not remove the need to assess whether the result works. Scholarly analysis of vibe coding describes this as a shift in effort toward context management and evaluation. You still need to decide what to build, provide enough context, judge the output, and take manual control when prompting no longer gives you confidence in the result.

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  • More suitable: exploratory prototypes or low-consequence experiments where you can test the result and discard it if it is not useful.
  • Needs greater care: projects that will be maintained, shared, or relied on beyond an experiment. Review and test the important parts before depending on them.
  • Not a shortcut around responsibility: accepting code without understanding it is the narrower, riskier meaning of vibe coding—not evidence that the software is correct or secure.

What vibe coding does not mean

Vibe coding does not mean that a person can describe any idea once and receive reliable, finished software. The workflow depends on repeated evaluation, and tools differ in what they can access or change. Nor does using natural language mean that code review is irrelevant: the less you inspect the implementation, the more you are relying on visible behavior and the less you may understand about how that behavior is produced.

It also does not imply that every AI-assisted programmer ignores the code. The stricter definition concerns accepting generated code without reviewing or understanding it. People can use the same conversational tools while taking a more deliberate approach—checking changes, running tests, and editing code directly when needed.

A simple way to begin responsibly

  1. Pick a small, clearly bounded idea that you can test yourself.
  2. Write down the behavior you expect, including a few cases that should work and what should happen when they do not.
  3. Find out whether your tool only generates suggestions or can read and change files or run commands.
  4. Ask for an initial implementation, then run it and compare the behavior with your expectations.
  5. Give specific feedback, make one meaningful revision at a time where practical, and test again.
  6. Before relying on the result beyond the experiment, review the consequential changes and decide whether you understand enough to maintain the software.

The useful mental model is a conversation wrapped around a feedback loop. AI can produce or modify code, but the person using it still supplies intent and context, observes outcomes, and decides whether those outcomes are acceptable.

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