AI coding assistants transformed software development in 2025, but using them effectively is still challenging. These tools, like Claude Code, require structured workflows rather than just prompts. The author emphasizes the need for clear planning before diving into code. He suggests starting with detailed specifications, iterating with the AI to flesh out requirements, and compiling everything into a comprehensive spec document. This upfront work sets a solid foundation, allowing both the developer and the AI to understand the project’s goals and constraints. Breaking tasks into small, manageable chunks is essential. Large, complex requests often lead to confusion and inconsistent outputs. The author recommends addressing one function or feature at a time, aligning with test-driven development practices. By generating structured prompt plans, developers can guide AI through each step without overwhelming it. This iterative approach minimizes errors and maintains context, ensuring a smoother coding process. Providing extensive context is vital for optimal AI performance. Developers should include relevant code, project constraints, and known issues when working with AI. Tools like Claude’s Projects mode can assist by incorporating entire codebases, but manual context packaging remains important. The author highlights using context-dumping utilities to provide essential information, ensuring the AI operates with complete knowledge of the task. Leveraging these strategies can significantly enhance the quality of AI-generated code, making the partnership between human and machine more effective.