Practical

Vibe Coding

Last reviewed: April 2026

A software development approach where you describe what you want in plain English and an AI model writes the code for you.

Vibe coding is the practice of building software by describing your intent in natural language and letting an AI model generate the actual code. Instead of writing syntax, you write instructions — and the AI translates your "vibe" into working programs.

Where the term comes from

The term was coined in early 2025 to describe a workflow that was becoming increasingly common: developers (and non-developers) using AI coding assistants not just for autocomplete or snippets, but to build entire features and applications from conversational descriptions. The "vibe" refers to communicating what you want at a high level without specifying implementation details.

How vibe coding works in practice

A typical vibe coding session looks like this:

You describe the feature or application you want to build in plain English: "Create a dashboard that shows our monthly sales by region with interactive charts."
The AI generates the code — HTML, CSS, JavaScript, backend logic, database queries — based on your description.
You review the output, test it, and provide feedback: "Make the charts responsive and add a date range filter."
The AI iterates on the code based on your feedback.

Who uses vibe coding

Non-technical professionals who want to build internal tools, prototypes, or automations without learning to code.
Experienced developers who use it to accelerate routine tasks, scaffold new projects, or explore unfamiliar frameworks.
Product managers and designers who build functional prototypes to validate ideas before involving engineering teams.

Limitations to understand

Quality varies: AI-generated code may work but be poorly structured, insecure, or inefficient. Review and testing remain essential.
Complex systems need expertise: Vibe coding works well for straightforward applications but struggles with complex architectures, performance optimisation, and edge cases.
Debugging requires understanding: When AI-generated code breaks, you need enough technical knowledge to diagnose the problem or describe it clearly to the AI.

The broader implication

Vibe coding is lowering the barrier to software creation significantly. It does not eliminate the need for professional developers, but it does mean that more people in an organisation can build tools and automations that previously required engineering resources.

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Why This Matters

Vibe coding represents a fundamental shift in who can build software within an organisation. Understanding its capabilities and limitations helps leaders decide when to use AI-generated code versus traditional development, and how to ensure quality and security in AI-assisted projects.

Related Terms

Agentic Coding

Using AI agents to write, test, and debug code autonomously — planning multi-step development tasks and executing them with minimal human intervention.

Large Language Model (LLM)

A type of AI trained on vast amounts of text to understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs.

Prompt Engineering

The skill of writing instructions to AI that consistently produce useful, accurate, high-quality output.

Automation

Using technology to perform tasks without manual human effort. AI automation goes beyond traditional rule-based automation by handling unstructured tasks like writing, analysis, and decision-making.

Tool Use (AI)

The ability of an AI model to call external tools, APIs, and services to perform actions or retrieve information beyond its built-in capabilities.

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