Vibe coding

Building software quickly through conversational prompts to an AI model.

Building software conversationally with an AI model, guided by intent and feel rather than by specification. Produces working prototypes fast, and rarely produces production-grade systems.

Building by prompting your way forward without an architectural plan. Fast to a demo, expensive to a business.

Building software by directing AI agents in natural language.

Using natural language prompts to have AI write software, no traditional engineering required.

Building software primarily by directing AI rather than writing every line by hand.

Generating apps by prompting AI, often producing code the user cannot read, maintain, or secure. The hard part moves onto the user rather than being removed.

Building software by prompting an AI tool with loose intent rather than engineering it deliberately. Great for MVPs and prototypes; produces maintenance nightmares at production scale.

Late-night, dopamine-fueled, AI-assisted product building — typically without security review, data governance, or a privacy policy in sight.

Building software through natural language prompts to an AI model, without writing traditional code. The term describes a workflow where the founder describes what they want and the AI generates the implementation.

AI-assisted development where a user describes what they want in natural language and an AI agent writes and iterates on the code — often without the user reading the code directly.

Building software through natural language prompts without writing traditional code, using AI tools like Claude or Cursor to translate ideas directly into functional products. Colin McIntosh launched three websites in one week using this approach.

The practice of prompting AI tools to generate code without deeply understanding or owning the architectural output. High velocity, low accountability. Works effectively for prototypes, demos, and internal tools where the cost of failure is low. Creates significant risk in production applications where security vulnerabilities, compliance failures, and untested edge cases surface as customer-facing problems. The term is sometimes pejorative but is more accurately a description of a risk profile than a quality judgment — the same tools used irresponsibly for production work are enormously valuable when applied appropriately.