What is the term Vibe Coding?

Vibe coding is a development approach that relies on large language models (LLMs) to generate code through natural language instructions, minimizing the developer’s involvement in the fine details of programming. Instead, the focus shifts to the “general intentions” of the product and its final outcome. The term became popular after Andrej Karpathy spoke about it in 2025, describing it as “surrendering to the vibe and forgetting that code even exists,” driven by the rise of tools like Cursor and Claude that make building features through dialogue fast and practical.

Background and Origin of the Term

In February 2025, Karpathy published a post describing a new style of programming based on natural language interaction with generative tools, with wide acceptance of AI suggestions without worrying about the code’s structure. The term quickly spread across media and the tech community. (X (formerly Twitter))
Tech and media coverage described this trend as a new job skill and a topic of discussion in Silicon Valley. (WIRED, Tom's Guide)

The Core Idea

Instead of writing code line by line, you describe what you want (“Create a settings page with a filter and CSV export…”), and the AI assistant generates the files and modifications. Then you give feedback (“Reduce the sidebar margin by half,” “Make the query use pagination”), and repeat the cycle until you reach the right “vibe.” This differs from literal “translation” or simple smart completion; its essence is rapid experimentation and accepting broad changes without early, precise engineering. (Simon Willison’s Weblog)

How is it Different from Traditional AI-Assisted Programming?

Traditional AI Coding: The developer writes most of the code and reviews AI suggestions carefully and deliberately.
Vibe Coding: The developer sets the goal and outcome, accepting broad suggestions from the tool, with later verification through running and testing rather than line-by-line inspection. Some experts argue that confusing the two undermines proper understanding of responsible use of intelligent tools.

Why Now?

Tool Maturity: Environments like Cursor and other AI-driven platforms have made on-demand file and project generation a practical reality.
Accessibility: Recent coverage highlights how non-specialists can now build small applications simply by conversing with models, sparking widespread interest.
Public Experiments: Blog posts and demos showcase prototypes built in this way (such as MenuGen). (karpathy)

Suitable Use Cases

Rapid Prototyping: Testing a product idea or feature in hours instead of weeks.
Personal and Small-Scale Tools: “Software-for-one” applications or scripts for repetitive tasks.
Learning New Technologies Quickly: Overcoming the barrier of starting with unfamiliar stacks (while being aware of the risks).

Risks and Challenges

Maintainability and Readability: Accepting broad changes may result in code that is difficult to understand later.
Quality and Security: Research and reports warn that generated code may introduce bugs and vulnerabilities if not carefully reviewed and tested.
Over-Reliance on Tools: Negative experiences with AI coding agents failing in critical tasks highlight the need for governance and safeguards.
Misunderstanding: Critics argue that the term may give the impression that “coding has become just a vibe,” while the serious work remains in testing, reviewing, and integration. (Simon Willison’s Weblog)

Practical Guide: How to Do Vibe Coding Responsibly (“Intend → Generate”)

Step 1 – Define the Goal as a Mini Specification Contract:
Write a concise functional description (“A monthly sales reports page with CSV export, sortable by country…”). Attach input/output examples, performance limits, and expected error messages. This improves output quality.

Step 2 – Manage a Structured Dialogue Session:
Break features into short tasks. Ask for a “file plan,” then “generate each file,” then “run tests.” Provide precise feedback on each iteration (“Reduce query time to <300ms,” “Replace caching with Redis”).

Step 3 – Quality Defense Line:

Write unit and integration tests early, let the tool generate them, then refine manually.
Enable static analyzers and security scanners (linting, SAST) in the CI pipeline.
Ask the tool to generate documentation, comments, and a short architecture file after each batch of changes.

Step 4 – Limits of Automation:

Never grant agents destructive permissions on production databases. Use isolated environments and “suggestion-only” mode.
Review diffs for sensitive changes even if you “accept all” in earlier stages.

Step 5 – Final Output:
Request “code cleanup” and “deduplication,” then “summarize architectural decisions,” and finally “generate a risk list with mitigation plans.”

Common Tools and Platforms in This Context

AI-Powered IDEs (such as Cursor) are heavily featured in recent coverage, showcasing pair programming with models.
General Conversational Assistants (ChatGPT/Claude/Gemini) for building quick prototypes and simple web interfaces.
Published Practical Examples such as the MenuGen project, which demonstrates the workflow from idea to prototype through dialogue.

Best Practices for Adopting Vibe Coding in Teams

Define a Clear Scope: Limit it to prototypes and low-risk features before moving to production.
AI Governance: Access policies, audit logs, secret protection, and no direct write permissions to production.
Mandatory Quality Contracts: Minimum test coverage, mandatory human review for security/performance-sensitive areas, and a pre-merge checklist.
Educate the Team on Differences: Not all “code generation” is vibe coding; precise understanding reduces false expectations and promotes responsible usage.

Quick Comparison

Aspect	Vibe Coding	Traditional AI-Assisted Programming	Low/No-Code
Starting Point	Dialogue / High-Level Goals	Developer writes, AI completes some parts	Prebuilt Components
Control Over Architecture	Low initially	High	Limited by platform templates
Speed to MVP	Very High	High	High
Long-Term Maintainability	May degrade if not governed	Relatively good	Platform-dependent
Production Suitability	With caution and governance	Higher	Depends on the system

When It’s Not Recommended

When working on critical systems or compliance requirements (health/finance/security) unless strong review and testing layers are in place.
When maintainability and institutional knowledge are more important than rapid prototyping.

Conclusion

“Vibe coding” is not a magic wand nor the end of traditional programming; it is a method that excels in idea exploration and accelerating prototypes, provided it is combined with testing and engineering discipline. It is best treated as a new lens in your toolkit: use it when you want to quickly reach the product’s desired outcome, then move to stricter engineering before going into production.