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AI Literacy: Vibe Coding

Introduction

Note: Users are strongly advised not to overtrust any code or output produced by AI tools. All AI-generated code should be thoroughly reviewed, tested, and validated before use, especially in critical or production environments. Vibe coding is best viewed as a collaborative tool—human expertise, careful oversight, and security awareness remain essential at every stage of the development process.

Vibe coding is a rapidly emerging programming approach where AI—primarily large language models (LLMs)—generates software code from natural language prompts. Coined by Andrej Karpathy in 2025, this paradigm enables users to describe what they want an application to do, and the AI generates the corresponding code. This makes coding accessible to a much broader audience, including those without traditional programming backgrounds, and shifts the programmer’s role from manual code writing to guiding, validating, and refining AI-assisted code.

It's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works. (Andrej Karpathy, 2025)

Key Benefits

Vibe coding offers several compelling advantages. Chief among them is accessibility, as it enables individuals without formal programming skills to create software by communicating their ideas in plain language. This accelerates prototyping and product development, with many teams reporting much faster project turnaround. Developers can thus focus on high-level design, creative problem-solving, and user experience, leaving the AI to handle syntax and repetitive tasks.

Experienced developers are also empowered, using vibe coding to quickly generate prototypes, explore new technologies, and automate mundane aspects of workflows. The creative and conversational nature of vibe coding increases engagement and can significantly reduce project barriers, while custom and personal-use tools become much easier to build.

Popular Tools and Platforms

Vibe Coding’s rise is accompanied by a range of AI-powered tools:

  • Cursor (Recommended), Windsurf (Recommended), Trae: Code editors centred on agentic coding, with rapid iteration features.
  • GitHub Copilot, Roo Code, Cline, Aider: Agentic coding within your familiar coding environments.
  • Replit (Recommended): Fast app prototyping and deployment via natural language.
  • Lovable, V0, Bolt: Purpose-built platforms that convert natural language or designs to functioning software.
  • ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), DeepSeek, Grok (xAI), Qwen (Alibaba): General-purpose LLMs used for code generation, debugging, and explanation.

Active communities on Reddit (e.g., r/vibecoding, r/cursor, r/ChatGPTCoding) and dedicated forums (e.g., Cursor Forum) provide support, share experiences, and discuss best practices.

Learning Resources

Vibe Coding 101 with Replit - DeepLearning.AI

This course teaches you how to build and deploy web applications using Replit and AI coding agents, focusing on the practice of Vibe Coding, where you guide the overall structure while the agent handles most of the coding. You'll learn strategies for collaborating effectively with AI, including precise prompting, structured workflows, and debugging, as you create a website performance analyser and a national park ranking app. By the end, you’ll have hands-on experience in agentic development and clear frameworks for building efficiently with coding assistants.

Vibe Coding Complete Tutorial and Tips - Cursor / Windsurf

This tutorial covers the essentials of Vibe Coding, leveraging AI-powered code editors like Windsurf, Cursor, and online tools to iteratively build software by collaborating with AI agents. It explains how to choose tools and languages, plan projects with AI, set up rules and version control, write and test code in small chunks, and apply best practices for maintainability and security—all with the help of AI assistants. The workflow centers on creating detailed plans, coding feature-by-feature with AI, using tests and version control, and continually refining both code and process, making programming more accessible and enjoyable for beginners and professional developers alike.

Tutorial videos | aider

This official video list showcases a variety of tutorials by different creators demonstrating how to use the AI coding assistant Aider for tasks such as setting up development environments, building complex full-stack apps and games, and integrating with other tools like Replit and Claude. These videos cover both beginner and advanced use cases, including incremental development, mobile voice coding, using AI for project planning, and working with local or hosted LLMs. Collectively, they highlight Aider’s versatility for AI-assisted coding, from starting projects with one prompt to updating existing codebases and enhancing productivity with automation.

Limitations and Challenges

Despite its promise, vibe coding introduces meaningful risks. AI-generated code may contain errors, inefficiencies, or security vulnerabilities, requiring thorough testing and human code review. Over-reliance on AI can erode fundamental programming skills and core software engineering principles. Debugging is also more challenging when users lack a deep understanding of the codebase, and inconsistent UI/UX can arise unless clear design guidelines are provided.

Other concerns include the quality and maintainability of AI-generated code, as well as the potential for misaligned output if prompts lack sufficient detail or context. Security remains particularly crucial: without proper input validation, authentication, and secrets management, applications are vulnerable to risks such as injection or data leaks. Since AI output depends on user prompts (“garbage in, garbage out”), precise requirements and oversight are essential. There’s also a need for accountability for errors or biases inherited from AI models.

Best Practices

To maximise vibe coding’s benefits and minimise pitfalls, follow these guidelines:

  • Give clear, specific prompts: Avoid ambiguity; detailed requests yield better results.
  • Break tasks into steps: Let AI handle one focused item at a time for more precise output.
  • Iterate incrementally: Start simple and add complexity in stages.
  • Employ version control: Regularly checkpoint progress to allow easy rollback.
  • Thoroughly review and test AI code: Don’t deploy without rigorous validation, particularly for security.
  • Adopt secure coding habits: Never hardcode sensitive info; use environment variables and authentication.
  • Combine AI with human expertise: Utilise vibe coding to accelerate development, but constantly review for quality, maintainability, and security.
  • Invest in prompt engineering: Good prompts are key to reliable AI output.
  • Document everything: Make sure code choices and project structure are clear for future collaborators.

Conclusion

In conclusion, vibe coding is transforming how software is created, unlocking access, speed, and creativity. Yet, it also demands a balanced approach—combining the power of AI with sound software engineering and human oversight—to ensure robust, secure, and maintainable solutions. As adoption grows, both individuals and organisations should strive to remain informed and responsible, blending new tools with established best practices for optimal results.