What is vibe coding? How it works, uses, and risks

The term was coined by AI researcher Andrej Karpathy in 2025. It is sometimes used more broadly for AI-heavy software development, but simply using an AI coding assistant does not necessarily make a workflow vibe coding.

Vibe coding shifts much of the development interaction from writing source code directly to describing what the software should do.

A person might ask an AI system to create an application, run the result, describe what is wrong or missing, and continue requesting changes until the software behaves as expected. The AI handles much of the coding, debugging, and modification behind those requests.

What distinguishes vibe coding is the shift from implementation details toward evaluating outcomes: does the feature work, does the interface look right, and does the application behave as expected? In stricter vibe coding, that shift can also mean understanding less of the resulting codebase.

Vibe coding usually follows a conversational development loop rather than a traditional write-review-debug cycle.

A typical workflow looks like this:

1. Describe the desired result. The user explains the application, feature, or change in natural language.
2. Generate the implementation. The AI creates or modifies the relevant code and files.
3. Run or preview the result. The user evaluates what the software actually does.
4. Describe problems or changes. Instead of manually editing the code, the user tells the AI what needs to change.
5. Ask the AI to revise the code. The model attempts to fix bugs, adjust behavior, or add functionality.
6. Repeat the cycle. Prompting, testing, and refinement continue until the result appears satisfactory.

Depending on the tool, the AI may also run commands, inspect errors, modify several files, or help deploy the application. The human increasingly directs the outcome while the AI handles more of the underlying work.

Vibe coding and AI-assisted coding overlap, but they are not necessarily the same development approach.

With AI-assisted coding, a developer may ask AI to generate boilerplate, explain unfamiliar code, suggest tests, refactor a function, or debug a problem. AI accelerates the work, but the developer still reviews the code, understands important technical decisions, and remains responsible for conventional engineering validation.

In vibe coding, more of the coding is delegated to AI, and the human may focus mainly on whether the software appears to work. Code-level scrutiny can be substantially lower.

There is no universally accepted boundary between the two terms. The main difference is how much engineering understanding and verification remains around the generated code, not which AI tool was used.

Vibe coding is most useful when quickly learning whether an idea can work matters more than establishing a long-term engineering foundation.

It can be practical for:

• Rapid prototypes. Turning a product idea into something interactive without first building a complete engineering setup.
• Proofs of concept. Testing whether a workflow, integration, or product concept is technically plausible.
• Personal tools. Creating small utilities for a narrow task where the consequences of failure are limited.
• Simple internal utilities. Automating straightforward workflows before deciding whether a more durable solution is necessary.
• Early product validation. Giving users or stakeholders something concrete to evaluate before making a larger development investment.
• Non-developer experimentation. Allowing people with limited programming experience to turn relatively simple ideas into working prototypes or applications.

Its main advantage is faster iteration. Speed matters less once the software has real users, integrations, sensitive data, and long-term maintenance requirements.

Vibe coding does not automatically produce insecure or low-quality software. The risks increase when generated code is accepted because the visible result works without enough engineering review and validation underneath it.

Generated code may contain weaknesses in authentication, authorization, secrets management, dependency choices, permissions, or data handling. These issues are easy to miss when evaluation focuses on whether a feature appears functional.

A polished interface does not show whether security boundaries have been implemented correctly.

Repeated AI-generated changes can introduce duplicated logic, inconsistent patterns, unnecessary complexity, or architectural decisions that nobody explicitly chose.

These problems may stay hidden at first and surface later when developers need to modify, refactor, or extend the codebase.

Software that works in a demonstration may still contain incorrect business logic, weak error handling, missing edge cases, or insufficient test coverage.

Generated tests also need review. A test suite created from the same assumptions as the code can confirm expected behavior while missing incorrect requirements or failure scenarios.

A codebase becomes difficult to own when nobody can confidently explain its structure or important technical decisions.

That can make debugging, refactoring, scaling, onboarding another developer, or transferring the product to a team more difficult. Fast initial generation can shift effort from writing code to understanding and verifying it later.

Yes, vibe-coded software can be used in production, but the fact that an application works is not evidence that it is production-ready.

Before real users depend on the product, the generated code needs the same kinds of engineering scrutiny as other software. Important areas include architecture, authentication and authorization, secrets and configuration, dependencies, automated tests, data handling, reliability, and maintainability.

How much verification is appropriate depends on what the application does. A small personal utility and a customer-facing system that processes payments or sensitive information do not carry the same consequences if something fails.

The closer software gets to real users, private data, financial transactions, or business-critical workflows, the less reasonable it becomes to rely only on whether the application appears to work.

For developers who want an additional verification layer before release, Mad Devs also describes a lightweight AI-assisted code audit workflow.