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Vibe coding is an AI-assisted way to build software by describing what you want, trying the generated result, and refining it through conversation. In its strictest form, you do this without reading the generated code. That conversational loop can feel like a new kind of syntax—and may help some people stay immersed in making—but it does not guarantee a psychological flow state or remove the need for technical judgment.
What is vibe coding?
Andrej Karpathy popularized the phrase in February 2025. In a post reproduced by software practitioner Martin Fowler, Karpathy described a style of coding in which people “fully give in to the vibes, embrace exponentials, and forget that the code even exists.” Fowler’s discussion of vibe coding uses that idea to draw a useful boundary: in the strict sense, a person prompts a large language model (LLM), tries what it builds, and asks for changes without inspecting the generated code.
Everyday usage is broader. People may call any conversational use of AI to create software vibe coding, including work where a developer does read and review the output. It helps to say which meaning you intend. The key shift is that the person expresses goals and evaluates results while the AI infers an implementation, rather than the person writing every instruction directly in a programming language.
What’s your definition of vibe coding?
A practical definition is: an iterative software-building workflow in which a person describes desired behavior or appearance in natural language, lets generative AI produce or change the implementation, and evaluates the result through use, inspection, or testing. Whether the person reviews the code separates strict vibe coding from the broader family of AI-assisted programming.
Calling flow state “the new syntax” is an interpretive frame for this shift in how people communicate intent to software. Traditional code gives a computer explicit, structured instructions; a natural-language prompt gives a model a goal to interpret. That can make the interface feel more immediate, but prompts are not formal program syntax: a model’s interpretation is probabilistic and can be incomplete or wrong.
How does the vibe-coding loop work?
Vibe coding is not simply one prompt followed by a finished application. It is a cycle of making a request, seeing what the system produces, and deciding what to adjust. Google product director Kelly Schaefer describes explaining what you want to make and what it should look like, then receiving something visible to use in Google’s interview about vibe coding. Microsoft Research’s 2025 report likewise summarizes studies that characterize the process through goal satisfaction and output verification, with testing and debugging taking up much of the interaction in one student study. The Microsoft Research report frames verification as part of the work, not an optional final step.
- Describe the goal. Explain what the software should do, who will use it, and any important constraints. Include examples or expected behavior where possible.
- Generate a first version. Ask the AI to create or modify the app, interface, or feature. Treat the result as a draft, not proof that the requirements have been met.
- Try it and verify behavior. Use the output, compare it with the intended result, and test important cases. A feature that looks right may still fail for other inputs or conditions.
- Refine the request. Describe what is wrong or missing, then ask for a targeted change. Repeat testing after meaningful changes.
- Review what matters before release. As the audience and stakes grow, shift from evaluating only visible behavior to checking implementation, security, reliability, and maintainability.
This cycle can create a sense of momentum: an idea becomes visible quickly, and feedback can be conversational. But the available evidence does not show that vibe coding reliably produces a measured flow state for everyone. Smooth interaction and deliberate verification need to coexist; Microsoft Research’s synthesis describes trust as dynamic and connected to repeated checking.
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Do you need to have any coding skills to vibe code?
Not necessarily to make a simple app or prototype. A person without traditional programming experience may be able to describe an idea and work with generated output. But the skills needed depend on what happens next: someone still has to judge whether the result works, handles mistakes safely, and is suitable for its intended users.
Expertise is not simply removed; some of it shifts toward supplying context, evaluating output, testing edge cases, and deciding when a result is fit for purpose. Microsoft Research’s report, summarizing a 2025 student study by Geng and colleagues, says low-context prompts accounted for 8.39% of prompts from advanced students versus 28.89% from introductory students. In that study, 90.37% of code-engagement activity was limited to interpretation. These are findings about the students and study summarized in the report, not a profile of all people who vibe code.
For a product intended for many users, Schaefer cautions that coding skill and precision still matter: “If you want to bring a vibe-coded app all the way to being a fully launched product that a lot of people can use, you still need coding skill and precision.” The distinction is not beginner versus expert; it is whether the task and its consequences call for deeper technical review.
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How is vibe coding different from reviewed AI-assisted programming?
Fowler distinguishes strict vibe coding from what he calls agentic programming: developers use AI to write or change code but still review it and care about its internal structure. In practice, people use “vibe coding” loosely, so the boundary is best understood as a difference in how much they inspect and control the implementation.
| Question | Strict vibe coding | Reviewed AI-assisted programming |
|---|---|---|
| What does the person evaluate? | Primarily whether the generated result behaves as intended; the person may not read the code. | Both behavior and the generated code, including its structure. |
| What work is it suited to? | Often a quick experiment or prototype where the consequences of failure are limited. | Work that must fit a codebase, technical constraints, or longer-term maintenance needs. |
| How is the result checked? | By trying it and asking for changes, with the risk that hidden defects go unnoticed. | By testing and debugging as well as reviewing implementation and relevant quality concerns. |
| Who remains responsible? | The person or team that chooses to deploy and support it. | The person or team that reviews, deploys, and supports it. |
The more an application depends on existing systems, strict requirements, or a large audience, the less adequate behavior-only evaluation becomes. AI can help produce code in either workflow; the difference is the human review and responsibility around it.
Does vibe coding make software development faster?
There is no single reliable speed figure that applies across projects. A 2026 state-of-the-art review by Michels, Abu Ghazaleh, Lazzari, Kassem, and Klein reports divergent results across study types: peer-reviewed field experiments found 26% more tasks per week, independent randomized trials measured a 19% slowdown, and team-level telemetry showed code-review time up 441%. The review’s abstract says the differences may reflect measurement method, scope, and time horizon. It is a preprint submitted to IEEE, and these secondary summaries are not settled causal estimates for every developer or task.
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Those results illustrate why “faster” needs a defined measure. Producing a visible prototype quickly is not the same as completing tested, maintainable software. Time saved while generating code can be offset by prompting, debugging, review, or later rework. Whether the workflow speeds a particular project depends on the task, the codebase, the quality bar, and how much verification is needed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you check before deploying AI-generated code?
Generated output should be treated like code from any source: test it against its requirements and review the risks that matter for its use. The Cloud Security Alliance’s note on AI-generated code labels itself unofficial AI-assisted research; it summarizes studies reporting vulnerabilities and discusses newer attack surfaces, but it is not an authoritative systematic estimate of risk. Its security note is a reminder to test and review before deployment, not evidence for a universal vulnerability rate.
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- Requirements: Test the expected behavior, including ordinary inputs and meaningful edge cases.
- Failure handling: Check what happens when inputs are missing, invalid, or unexpected.
- Security: Review how the software handles data, permissions, and external inputs before real users rely on it.
- Maintainability: For code that will evolve, inspect whether the implementation is understandable and fits the surrounding system.
- Operational ownership: Know who will fix defects, respond to incidents, and maintain the app after launch.
Responsibility does not transfer to the model or tool. The Associated Press quoted Cat Wu, project manager for Anthropic’s Claude Code, saying: “We definitely want to make it very clear that the responsibility, at the end of the day, is in the hands of the engineers.” AP’s September 2025 report gives that accountability a clear practical implication: the people deploying software remain responsible for the result.
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When is the “new syntax” a useful way to think about it?
The metaphor is useful when it describes a change in the interface between an idea and an implementation. Instead of first translating every intention into formal code, a person can state a goal, inspect an output, and steer the next attempt. That can lower the barrier to experimenting and make creative iteration feel direct.
It becomes misleading if it suggests that natural language is precise by default, that every user enters flow, or that code review and engineering skill no longer matter. The conversational loop changes how intent is expressed; it does not eliminate uncertainty, verification, or responsibility.
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