Incorporating AI tools into a software development workflow can initially feel frustrating and unproductive. The author, after facing setbacks, discovered that effectively using AI requires the same principles that have guided software development for years: breaking tasks into manageable parts, understanding the problems before attempting solutions, evaluating what works, and iterating based on feedback. This approach can help developers harness AI tools to enhance their productivity rather than hinder it. The author outlines a four-phase cycle for effective AI coding: prompting, planning, producing, and refining. Prompting is crucial; the quality of the AI's output is directly tied to the clarity and specificity of the input. For effective prompting, managing context is essential. AI tools can lose quality as they accumulate context, leading to errors. The author recommends wiping the AI's context between tasks to maintain output quality and suggests storing useful knowledge in markdown files for reference instead of relying on the AI's memory. When crafting prompts, breaking tasks down into discrete actions is key. For instance, instead of giving a vague instruction like "implement an authentication system," the author illustrates how to guide the AI with specific, step-by-step requests. For more complex tasks, chaining prompts can help by using a separate AI to draft detailed instructions. Additionally, finding effective patterns and reusing successful prompts can streamline workflow. The author emphasizes that these practices can significantly improve how developers interact with AI tools, ultimately leading to better results in coding projects.