Vibe coding: what it is and how to bring AI into development

🔍 What vibe coding is (and what it is not)

The term vibe coding refers to a development practice in which the programmer works closely with an AI assistant (Claude Code, GitHub Copilot, Cursor, Gemini Code Assist), using it as a non-human pair programmer. The flow is iterative: the developer describes the intent, the AI proposes code, the developer reads it, critiques it, and refines it.

It is not occasional prompt engineering, not copy-paste from ChatGPT, and not autopilot. It is a change of method that redefines who does what: the AI writes the first draft, the developer decides. To work in a company rules are required.

• The AI suggests — the human validates and signs off.
• Vibe coding sessions are tracked, not improvised.
• Every project has clear policies on which repositories and which data may be exposed to AI assistants.

🎯 Why vibe coding is strategic in 2026

Three factors are pushing companies toward vibe coding: pressure on delivery timelines, difficulty in finding senior developers, and the growing complexity of legacy codebases. A well-configured AI assistant cuts in half the time spent reading other people's code, writing boilerplate, performing mechanical refactoring, and translating intent into syntax.

Data collected from several international surveys (GitHub, JetBrains, Stack Overflow) converge: teams that have adopted a structured vibe coding workflow report a measurable increase in merged pull requests per developer and a reduction in onboarding time on new projects.

🗺️ How to introduce it: the 4-phase framework

A serious introduction of vibe coding in a company follows four distinct phases. Skipping one is the number one cause of failed adoptions: the team receives Copilot licences, nobody defines the rules, and six months later everyone is working the same way as before — except now there are commits signed "// TODO: the AI generated this, I haven't read it".

🛠️ Recommended tools for vibe coding in production

There is no single right tool: the choice depends on budget, privacy constraints, and the existing ecosystem. In contexts where data must stay on-premise I use local models (Ollama, vLLM) integrated into development workflows. For cloud-friendly scenarios Claude Code and GitHub Copilot are the two workhorses, often used in combination.

• Claude Code — agent with real tools (read/write/bash), ideal for multi-step tasks.
• GitHub Copilot — contextual autocompletion, integrated in IDEs.
• Cursor / Windsurf / VSCode (with plugins) — editors designed for vibe coding.
• Ollama + Continue.dev — privacy-first option, models running locally.
• MCP (Model Context Protocol) — open standard for giving models access to enterprise tools.

⚠️ The three most common mistakes

When I support companies in adopting vibe coding, I always see the same three mistakes. The first is handing out licences without policies: every developer invents their own workflow, quality becomes unpredictable. The second is not measuring: without KPIs the project tells its own story and nobody knows if it is working. The third is leaving the AI unsupervised on critical code: the assistant is very good at writing plausible code that nonetheless fails to handle the edge case of the customer with the legacy database migrated from the old ERP system back in 2008.

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