Can AI Turn Coding into a Conversation? Inside Vibe Coding

Definition

Vibe coding is a development approach where a natural‑language prompt is given to a large language model (LLM) and the model generates most of the source code, turning development into a conversational workflow.

Origin

The term was coined by Andrej Karpathy in early 2025 after the release of ChatGPT in 2022. Y Combinator’s 2025 winter batch reported that roughly 25 % of startup codebases are largely AI‑generated.

Typical workflow

Describe the desired application in plain language (e.g., “a skincare blog with a homepage, article page and a simple editor”).

The LLM creates the project structure, UI components, and business logic.

Developer runs the generated code, tests functionality, provides feedback, and iterates until the prototype meets expectations.

Tooling

Platforms that expose conversational interfaces and can generate full projects include:

Bolt – https://bolt.com Replit – https://replit.com Webflow – https://webflow.com Notion – https://www.notion.com AI assistants such as ChatGPT, Claude, Gemini, Grok, Cursor, GitHub Copilot.

Comparison with traditional and low‑code development

Traditional development requires writing each line in languages such as JavaScript, Python, PHP, or C++, handling debugging, performance, and security manually. Low‑code/no‑code tools let users assemble applications from visual components within a fixed framework. Vibe coding sits between these extremes: natural‑language prompts generate complete code, but the resulting code may lack consistency, auditability, and security without expert review.

Limitations and risks

AI‑generated code can contain hidden bugs, security vulnerabilities, or inconsistent design patterns.

Large‑scale or data‑sensitive applications require rigorous human oversight.

Developers still need basic knowledge of how to copy, paste, save, and run the generated files (e.g., using Xcode for iOS, Android SDK, or a local web server).

Use cases

Vibe coding is most effective for rapid prototyping, personal tools, experimental projects, and simple web apps such as recipe organizers, to‑do lists, micro‑blogs, or budget trackers. It is less suitable for browser extensions, enterprise systems, or products that must handle sensitive user data.

Best practices

Keep AI‑generated projects small and controllable so that experienced engineers can review every decision before release. Treat the LLM as a “co‑pilot” that accelerates scaffolding, but retain responsibility for code review, security assessment, and architectural decisions.

Conclusion

Vibe coding lowers the barrier to building simple applications, but human expertise remains essential for evaluation, accountability, and ensuring production‑grade quality.