🤖 AI Summary
In a recent article, a developer shared their innovative workflow using Claude Code, emphasizing a unique separation of planning and execution that contrasts with typical AI coding practices. Instead of directly prompting Claude to write code, they initiate their tasks with a thorough research phase, requiring detailed markdown documentation of findings before any coding begins. This approach ensures a deep understanding of the codebase and prevents foundational misunderstandings that could lead to flawed implementations. By meticulously reviewing the generated research, the developer highlights the importance of accuracy to avoid the "garbage in, garbage out" phenomenon that often plagues AI-assisted coding.
Following the research, the developer requests a comprehensive implementation plan, which they annotate with domain-specific insights, corrections, and exchanges with Claude in an iterative cycle until satisfied. This phase ensures that the plan aligns perfectly with the project’s architecture before implementation. Once the plan is validated, the developer issues a commanding prompt for execution, streamlining the implementation process and maintaining a supervisory role for quick corrections. This structured yet flexible method showcases how the AI coding tool can enhance productivity while ensuring the developer stays firmly in control of architectural decisions, ultimately resulting in more efficient and reliable code generation in complex projects.
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