Vibe coding: programming through conversation with artificial intelligence (2506.23253v1)

Abstract: We examine "vibe coding": an emergent programming paradigm where developers primarily write code by interacting with code-generating LLMs rather than writing code directly. We analysed a curated set of videos depicting extended vibe coding sessions with rich think-aloud reflections. Using framework analysis, we investigated programmers' goals, workflows, prompting techniques, debugging approaches, and challenges encountered. We find that vibe coding follows iterative goal satisfaction cycles where developers alternate between prompting AI, evaluating generated code through rapid scanning and application testing, and manual editing. Prompting strategies blend vague, high-level directives with detailed technical specifications. Debugging remains a hybrid process combining AI assistance with manual practices. Critically, vibe coding does not eliminate the need for programming expertise but rather redistributes it toward context management, rapid code evaluation, and decisions about when to transition between AI-driven and manual manipulation of code. Trust in AI tools during vibe coding is dynamic and contextual, developed through iterative verification rather than blanket acceptance. Vibe coding is an evolution of AI-assisted programming that represents an early manifestation of "material disengagement", where practitioners orchestrate code production and manipulation, mediated through AI, while maintaining selective and strategic oversight.