Vibe coding is a way of building software by describing what you want to an AI tool, then refining the result through prompts and trial runs. In its stricter sense, it means accepting AI-generated code without reviewing or understanding it. That distinction matters: an AI can help you build a prototype, but a successful demo does not prove the software is safe, reliable, or ready for other people to depend on.
What vibe coding means
In everyday use, vibe coding describes an outcome-first approach: you tell an AI coding tool what you want, it generates much of the implementation, and you guide changes through follow-up prompts. IBM describes it as a loosely defined software-development practice in which people prompt AI tools to generate code rather than write all of it themselves. IBM’s overview was updated on July 24, 2026.
OpenSSF uses a narrower definition: “Vibe coding is the process of generating and accepting AI-generated code without reviewing it or understanding it, ‘instead relying entirely on results and follow-up prompts to guide changes’.” Its glossary credits Andrej Karpathy with coining the term in February 2025. The glossary also reports his description of the approach as giving in to the “vibes,” not reading code changes, and pasting error messages back to the AI without comment.
These meanings are related but not identical. Using AI to write code while carefully reviewing it is AI-assisted development; it does not fit OpenSSF’s stricter definition. In practical conversation, people may still call both approaches vibe coding, so it is worth asking whether the developer actually understands and checks the generated code.
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How to try vibe coding on a small project
Start with a project that is easy to discard or fix, and keep the first version’s purpose narrow. The following workflow is practical guidance, not an official checklist or a guarantee that the result will be production-ready.
- Choose a low-consequence project. A personal script, mock-up, or limited internal experiment is a better first attempt than software that handles sensitive information or affects other people.
- Describe the outcome before asking for code. Explain who will use the project, what the most important behavior is, and what should happen in a few representative situations. Ask the AI to outline a small implementation first so you can spot an unsuitable plan before it grows.
- Build in short cycles. Ask for one feature, run the result, and report what you actually observed. Include the exact error or unexpected behavior where possible; then make one focused change and try again. This prompt-run-observe-refine loop is central to the OpenSSF definition.
- Check more than the happy path. Try ordinary use as well as boundary cases, such as empty input, unexpected formats, or repeated actions. You can ask the AI to propose tests, but a claim that tests pass is not evidence unless the tests were actually run and their results checked.
- Review before sharing or deployment. Check the code, dependencies, data handling, permissions, secrets, and what happens when something fails. If you cannot assess those points, ask someone qualified to review the project before others rely on it.
When vibe coding fits—and when it does not
The main decision is not whether AI wrote the code; it is how much risk you are willing to accept without someone taking responsibility for understanding and maintaining it. Martin Fowler recommends vibe-coded software chiefly for disposable projects used by their author or a close group of collaborators who understand and accept the risks. He cautions against forgetting about more complex, widely used, or consequential software. Fowler’s discussion was published on May 21, 2026.
- Reasonable starting point: a throwaway prototype, a personal utility, or a contained experiment whose failure is easy to recover from.
- Needs careful review before wider use: a prototype being shared beyond its creator or a small group, or any project that will be maintained over time.
- Do not rely on prompts alone: software involving credentials, personal information, payments, safety-sensitive uses, or strangers who depend on it. These cases call for testing and security review proportionate to the consequences, plus a person responsible for ongoing maintenance.
IBM notes that generated software still needs engineering effort before production. Palo Alto Networks likewise highlights hidden code threats and software-supply-chain complexity. A convincing interface or successful demo shows that a path through the app worked; it does not establish that dependencies are trustworthy, sensitive data is handled correctly, or failures are safe. Palo Alto Networks’ guide was published on May 27, 2026.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check before calling a prototype ready for review
“Ready for review” is a better milestone than “finished” when an AI has generated much of the implementation. It means there is a concrete result another person can inspect—not that the software is approved for production.
- Purpose: Can you explain what the prototype is intended to do and who should use it?
- Reproducibility: Can another person run it and see the behavior you described?
- Evidence: Have the main use cases and relevant boundary cases been tried? Are test results based on tests that were actually executed?
- Code and dependencies: Is someone able to inspect what the generated code and its external packages do?
- Data and access: Are secrets kept out of shared code, and are data handling and permissions understood?
- Failure and ownership: Is it clear what can go wrong, how to stop or roll back the prototype, and who will maintain it if it continues?
If you cannot answer the questions about code, dependencies, data, or access, the prototype needs a capable reviewer before it is shared or deployed. A prototype that passes a demo is a candidate for review, not proof of secure or maintainable software.
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