Writing effective specifications for AI agents requires clarity and structure. Start with a concise high-level vision, allowing the AI to expand it into detailed specifications. This approach ensures the AI has a clear mission and can elaborate on the details without straying off course. Make it a collaborative process by providing an initial outline, which the AI can then develop into a comprehensive spec. Use tools like Claude Code's Plan Mode to focus on planning before moving into coding; this restricts the AI to read-only operations initially, promoting thoughtful preparation. A well-structured spec resembles a professional Product Requirement Document (PRD) or System Design Document (SRS). It should include specific sections that cover commands, testing frameworks, project structure, code style, Git workflow, and boundaries. For instance, instead of vague statements, specify exact commands and frameworks, like "npm run build" or "React 18 with TypeScript." This specificity helps the AI generate accurate code. Maintain a consistent format, using clear headings or tags to improve readability and parsing by AI models. Keep the focus on user needs and project goals in your specifications. Initially, prioritize what the user needs and why, rather than detailing every technical aspect. This approach helps the AI remain oriented toward user experience and success criteria. Regularly review and refine the AI-generated specifications to ensure they align with your vision and correct any inaccuracies. By treating specs as living documents that evolve alongside the project, you can enhance both the planning and execution phases effectively.