The author emphasizes a methodical approach to using AI for coding, moving away from the instinct to have AI generate code immediately. Instead of rushing to solutions, the author treats AI as a design partner, focusing on understanding the existing system and documenting that understanding before writing any code. This process involves multiple steps: analyzing the system, capturing insights in a markdown document, and clearly articulating new requirements. The goal is not just to produce code but to ensure clarity and maintainability, especially in large codebases. A key part of the workflow is creating durable artifacts that capture reasoning and decisions. The author highlights the importance of identifying potential issues early, such as complexities related to user subscription tiers, which could affect new features. By outlining work in small steps and validating each phase, the process becomes more manageable and reduces the likelihood of errors. The author notes that this approach, while slower than simply generating code, results in a clearer understanding and better alignment with the design intent. The author also stresses the importance of written communication in this collaborative process with AI. Clear prompts and feedback lead to better reasoning from the AI, ultimately producing higher quality outcomes. The analysis document serves as a living record of the project, capturing intent and decisions, which helps maintain control over the design. This structured approach aligns with how effective engineering teams operate, prioritizing shared context and incremental progress over rushed implementations.