AI Coding Workflow Best Practices
After building dozens of projects with AI coding tools, these are the practices that consistently produce better code, faster development cycles, and fewer debugging sessions.
Plan Before You Prompt
The most common mistake in AI-assisted development is diving straight into coding without a clear specification. AI coding tools are incredibly good at implementing specifications — they're much worse at guessing what you want. Spend 20-30 minutes on planning (research, PRD, technical design) before writing your first prompt.
A well-written AGENTS.md or CLAUDE.md at the root of your project is worth hours of iterative prompting. It tells the AI your coding standards, architecture patterns, naming conventions, and project context — once, upfront, instead of repeatedly in each conversation.
Work in Small, Testable Increments
Don't ask AI to build entire features in one go. Break your implementation into small, testable units. Ask the AI to implement one function, one API endpoint, or one component at a time. Review and test each piece before moving to the next. This keeps errors small and easy to fix.
Use your PRD's user stories as natural implementation units. Each story becomes a self-contained task that you can specify precisely, implement, test, and check off before moving to the next.