AI coding assistants work best through an iterative workflow rather than one-shot prompts. The key is providing comprehensive context (project background, constraints, relevant code), requesting a plan before implementation, generating code in small steps with defined roles (planner, implementer, tester, explainer), reviewing output with AI-assisted tools, writing tests immediately, and debugging systematically. Common pitfalls include context drift in long conversations, API version mismatches, and over-reliance on AI without understanding the output. The workflow emphasizes treating AI like a new teammate who needs explicit briefing, keeping changes small and reviewable, and maintaining human oversight throughout the process.