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Vibe Coding

Définition

Vibe coding est un style de développement logiciel où l'on travaille de manière itérative avec l'assistance de l'IA: on décrit son intention en langage naturel, get code or edits from an LLM or coding tool, then refine by feedback and context rather than writing every line from scratch. The “vibe” is the loose, exploratory flow—you steer by intent and feel, and the model fills in implementation details.

It contrasts with fully spec-first or plan-then-code approaches (par ex. spec-driven development): you often start with a rough idea and let prompt engineering, agents, and tools (par ex. Cursor, Claude Code) suggest and edit code. Useful for prototypes, scripting, and tasks where speed and iteration matter more than upfront conception.

Comment ça fonctionne

You give the model (or IDE tool) context: open files, cursor position, or a short prompt (“add a test for this”, “refactor to use async”). The model returns suggested code or diffs; you accept, edit, or reject and optionally add feedback (“use a different library”, “make it shorter”). The loop repeats until the result matches what you want. Tools often provide project-aware context (indexed codebase, RAG-style récupération) so suggestions stay relevant. Success depends on clear intent, good tooling, and knowing when to take over or refine the output.

Cas d'utilisation

Vibe coding convient quand on veut avancer vite avec l'IA et qu'on accepte d'itérer plutôt que de fixer la spécification d'abord.

  • Prototyping and scripting (par ex. one-off scripts, small tools)
  • Boilerplate, tests, and refactors where the intent is easy to state
  • Learning or exploring a codebase by asking the AI to implement or explain
  • Combinaison avec des agents ou des agents autonomes qui écrivent et éditent du code à partir de descriptions

Avantages et inconvénients

ProsCons
Fast iteration and less typingCan obscure understanding if you never read the code
Good for exploration and learningMay produce brittle or overfitted code without review
Low friction for small tasksHard to scale to large, consistent systems without specs
Works well with agents and IDEsDepends on model quality and context

Documentation externe

Voir aussi