1. Preparation & Understanding

The effectiveness of AI-assisted coding is fundamentally constrained by the clarity and completeness of what you bring to the interaction. AI models cannot compensate for gaps in your understanding of the problem domain, your inability to recognize appropriate solutions, or your unfamiliarity with relevant tools and conventions. These limitations arise because AI tools lack true domain expertise; they pattern-match from training data rather than reason from first principles, and because they cannot assess whether their outputs align with field-specific best practices you haven’t mentioned.

The three rules in this section address these constraints by emphasizing preparation before implementation. Without this foundational preparation, you risk “vibe coding”: accepting AI-generated code or solutions you cannot evaluate, debug, or maintain. The investment in understanding your problem space, developing programming fluency, and selecting appropriate tools pays dividends throughout the development process by enabling you to direct AI assistance effectively and critically evaluate its outputs.