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Vibe coding can make implementation feel fast, but a prompt is not a substitute for deciding what the software should do. Before asking an AI assistant to generate code, clarify the problem, define observable behavior and constraints, and decide how you will check the result. Then use the assistant iteratively: generate, inspect, test, and revise.
What vibe coding does—and what it leaves to you
Vibe coding describes guiding an AI assistant in natural language to generate, refine, and debug an application. Google Cloud traces the term to Andrej Karpathy in early 2025. In practice, it is an iterative process, not simply a matter of sending one prompt and accepting whatever comes back. Google Cloud’s overview explains the broad approach, while a 2026 multivocal literature review describes how the practice is discussed across research and grey literature.
The assistant can produce implementation, but you still need to set the target and judge whether the result meets it. That shifts work toward expressing intent, supervising generated changes, and validating behavior. If “done” has not been defined, fast code generation can just make it faster to build the wrong thing.
What to settle before you ask for code
There is no universally validated pre-build sequence. A useful workflow, synthesized from specification-driven guidance and practical recommendations, is to make the following decisions explicit before implementation:
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- Problem and user: What problem are you solving, and who needs the software?
- Observable behavior: What should a user be able to do, and what should happen in response? Describe outcomes a person can verify rather than relying on vague goals such as “make it intuitive.”
- Constraints: Record relevant dependencies, technical limits, and other requirements that could change the implementation.
- Acceptance criteria: State what must be true for the feature or application to count as working. Include important cases beyond the expected, happy-path use.
- Architecture: Identify consequential design choices that should be reviewed before code generation, rather than letting an assistant make them invisibly by default.
- Small tasks: Break the work into pieces that can be generated, inspected, and checked independently.
These are practical questions, not a formal standard or a guarantee of better productivity. A 2026 Zenodo paper presents an intent-first, specification-driven approach but explicitly does not claim a controlled productivity result. Its example supports considering requirements before implementation; it does not establish that this workflow will make every project faster.
How to work with the assistant once the brief is clear
- Give the assistant the context. Include the problem, intended users, behavior, constraints, acceptance criteria, and the current task. Keep the request focused on a checkable piece of work.
- Review what changed. Inspect the generated code and the affected application components or routes. Check whether dependencies, data structures, or design decisions changed in ways the task did not call for.
- Run checks against the stated behavior. Test the acceptance criteria, including relevant cases outside the happy path. Do not treat a successful generation or a plausible-looking interface as proof that the software is correct.
- Revise from observed results. Describe what failed or differs from the intended behavior, then ask for a focused change. Repeat the review and checks after the next generation.
This cycle reflects the iterative character of vibe coding described in the literature review and practical guidance. The developer remains responsible for deciding whether generated behavior is correct and whether the changes are safe to keep.
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What review should cover before deployment
The amount of review depends on the project. For an application moving toward live deployment, TechRadar Pro’s practical guidance points to areas worth checking rather than a mandatory checklist for every build:
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- Application components and routes affected by the changes.
- The data model and any changes to how information is stored or handled.
- Dependencies introduced or updated during generation.
- Separation between environments, such as development and production.
- Tests that cover failures and less common paths, not only a successful first run.
- Backup and recovery arrangements appropriate to the application.
These checks matter because a 2026 state-of-the-art review describes uneven capabilities across tasks, including weak fault detection and documentation that can be difficult to audit. That is a qualitative review synthesis, not a quantified failure rate. It is a reason to inspect and test generated work, not evidence that every AI-generated build contains a defect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—show
A 2026 multivocal literature review retained 47 sources: 28 peer-reviewed and 19 grey-literature sources. That describes the review’s corpus; it is not a measure of how many developers use vibe coding or how productive they are. The available evidence supports treating vibe coding as an iterative practice in which clear intent and human validation remain important. It does not establish that planning improves productivity on every project, or that one pre-build method guarantees a successful result.
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