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Stage 1: Reality Check

Understanding the limitations and best practices of AI coding

Why This Stage Matters​

After the excitement of vibecoding, it's crucial to understand what AI can and cannot do. This stage helps you develop realistic expectations and avoid common pitfalls.

This comprehensive masterclass from Riley Brown takes you from beginner to expert in AI coding. Learn how to write effective prompts, understand AI limitations, and build real projects with AI assistance.

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What You'll Learn​

At this stage, you'll:

  • Understand AI coding limitations
  • Learn when to rely on AI vs. your own skills
  • Recognize AI hallucinations and errors
  • Develop better prompting techniques
  • Know when AI is helping vs. hindering

Key Concepts​

AI Strengths​

  • Boilerplate code generation
  • Common patterns and syntax
  • Quick prototyping
  • Code explanation

AI Limitations​

  • Complex architecture decisions
  • Novel problem-solving
  • Security-critical code
  • Performance optimization

Understanding AI Coding Limitations​

  1. Andrej Karpathy on AI Coding

    • Why recommended: Direct insights from a leading AI researcher on the realistic capabilities and limitations of AI coding assistants
    • Category: Expert Opinion

  2. Simon Willison: Things I learned about LLMs

    • Why recommended: Practical, hands-on experience from someone who actively codes with LLMs daily
    • Category: Practical Guide

  3. The False Promise of AI Coding Assistants

    • Why recommended: Realistic perspective from Stack Overflow on what AI can and cannot replace in software development
    • Category: Industry Analysis

  4. When AI Coding Goes Wrong

    • Why recommended: Research-backed examples of AI hallucinations and how to recognize them
    • Category: Research

Best Practices​

  1. GitHub Copilot Best Practices

    • Why recommended: Official guidance on effective AI coding practices from GitHub
    • Category: Best Practices

  2. OpenAI's Prompt Engineering Guide

    • Why recommended: Foundational techniques for getting better results from AI models
    • Category: Technical Guide

Critical Thinking​

  1. The Danger of Trusting AI Code Blindly

    • Why recommended: Real-world case study on the risks of uncritically accepting AI-generated code
    • Category: Case Study

  2. Testing AI-Generated Code

    • Why recommended: Martin Fowler's insights on verifying and validating AI-generated code
    • Category: Testing Strategy

What's Next?​

Ready to level up? Move to Stage 2: Context & Architecture to learn how to structure projects for AI collaboration.

Need help? Check our Resources page for additional learning materials!