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What does “vibe coding” mean?
At its broadest, vibe coding means using natural-language instructions to guide an AI tool as it generates or changes software. You describe the goal, inspect or run the result, explain what should change, and repeat. The tool may supply code in a chat, edit files in an existing project, or operate as an agent with permission to inspect files and run commands.
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The phrase also has a narrower meaning. Martin Fowler’s explanation describes the original, loose version as prompting a large language model, trying its output, and requesting changes without looking at the generated code. Fowler attributes the term to Andrej Karpathy in February 2025. Karpathy’s description, reproduced by Fowler, was: “I’m building a project or webapp, but it’s not really coding – I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.” Martin Fowler’s account of vibe coding
In everyday use, people may apply the label to more careful AI-assisted development, too. It helps to distinguish the two: relying on output without inspecting it is not the same as using AI to draft code that a person reviews and tests.
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How does vibe coding work?
The basic process is a feedback loop. A beginner might ask for a simple page, preview it, point out a specific problem, then check the revision. A more capable coding agent may also inspect a project’s files or run commands, so the process can involve changes beyond the code shown in a chat window.
- Describe a bounded goal. Say what the software should do and who it is for. For example, request a simple page that explains a service and includes a contact form.
- Give relevant constraints. Include details such as the intended users, required behavior, and whether the project will handle sensitive information.
- Ask the tool to generate or modify the project. Depending on the tool, it may return code in a conversation or act directly on project files.
- Run or preview the result. Check whether it behaves as requested, rather than judging only by how the code or interface looks.
- Report a specific issue and request a change. A focused instruction—such as identifying a form that accepts invalid input—is easier to assess than a vague request to “make it better.”
- Verify the revision. Test the changed behavior and check that the correction has not broken something else.
Google’s explanation presents natural-language iteration as a way to build and refine an application. Mistral’s documentation describes a more agentic workflow in which an AI can inspect project files, run commands, and write code under supervision. These are different levels of access, not just different ways to phrase a prompt. Google’s explanation of vibe coding; Mistral documentation on coding agents; Google’s guide to how vibe coding works
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How is vibe coding different from ordinary AI-assisted coding?
The key difference is oversight, not whether AI wrote the first draft. In the loose sense of vibe coding, a person may accept code without reading it. In more deliberate AI-assisted development, a person reviews the changes, runs tests, and takes responsibility for deciding whether the software is fit for use.
| Workflow | What the person does | What it establishes |
|---|---|---|
| Loose vibe coding | Describes a goal, tries the output, and may request changes without reading the generated code. | A successful-looking result does not establish that the implementation is correct or safe. |
| Reviewed AI-assisted development | Uses AI to generate or change code, then reviews, runs, and tests the changes. | Review and testing provide oversight, but they do not automatically guarantee that every defect has been found. |
Tool permissions matter as well. A chat-only assistant and an agent with access to project files or a shell can take different actions. Before using an agent, understand what it can read, change, and run; a mistaken or ambiguous request can have wider effects when the tool can act directly in a project.
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What can go wrong?
AI-generated software can be plausible on screen while containing hidden defects. Relevant concerns include incorrect behavior, unfiltered input, exposed secrets, insecure handling of data, and code that is difficult to maintain. The UK National Cyber Security Centre (NCSC) emphasizes that the level of risk depends on what is being built and how much oversight is applied. NCSC guidance on vibe coding
A 2026 academic study reports patterns including placeholder logic, unfiltered input, and exposed secrets in the applications it examined. Those findings describe that study’s corpus; they are not a universal defect rate or proof that every AI-generated application has those problems. The study also says stronger models and better prompts may reduce, but do not eliminate, risk. 2026 study of AI-generated applications
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Twilio’s July 2025 guidance advises against using vibe coding for production applications, citing concerns about complexity, maintainability, security, and accuracy. The NCSC’s June 2026 guidance takes a risk-based view. Taken together, these positions support a practical distinction: the “vibe coding” label alone does not decide whether a project is acceptable, but software used in production needs engineering review and controls appropriate to what it does. Twilio guidance on vibe coding; NCSC guidance on vibe coding
When is vibe coding a reasonable fit?
It can be useful for early exploration and quick prototypes, particularly when the output is easy to inspect and the consequences of an error are limited. For example, a draft interface can help clarify an idea before someone commits to a full implementation.
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More caution is warranted when software handles sensitive information or performs consequential operations. A prototype and a deployed service are not equivalent: deployment can expose users and data to defects that a quick preview would not reveal. The appropriate level of review depends on the application and the tool’s access, not just on how quickly a first version was generated.
How to use AI coding tools more responsibly
- Keep the first request small and specific. State the expected behavior so you can check whether the result meets it.
- Explain relevant context. Tell the tool who will use the software and what kinds of data or actions are involved.
- Know what the tool can access. Distinguish a tool that only returns code from one that can inspect files, edit a project, or run commands.
- Review changes. When the tool edits files, examine what changed and why, rather than relying only on a preview.
- Test more than the happy path. Check normal use, boundary cases, and failure cases; a successful run is not a complete correctness check.
- Keep qualified human oversight for higher-risk work. Security, privacy, access control, and consequential behavior need appropriate review before software handling sensitive data or important operations is deployed.
These practices reflect the sources’ emphasis on supervision, validation, and risk-based oversight; they are practical guidance, not a universal certification standard.
How to compare vibe-coding tools and workflows
Rather than choosing on the label “AI coding assistant,” compare what the tool can do and how you can supervise it:
- Does it only reply with code, or can it act inside a project?
- Can it read project files, run shell commands, or use other tools?
- How can you inspect and supervise its changes?
- What testing and security checks are available in the workflow?
Those differences affect both convenience and the consequences of an incorrect instruction. The sources cited here establish workflow distinctions, but do not provide a current independent comparison of commercial products, prices, or plan limits.
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