Advice for coding with AI
Blog post from WorkOS
More folks are picking up coding than ever before thanks to advancements in AI. When using AI tools, it's essential to never delegate understanding and be responsible for the architecture, data flow, and each component's role. This means using AI to speed things up but not replacing real comprehension. Additionally, providing explicit "What" and "How" when prompting AI helps keep code generation aligned and avoids wasted cycles on irrelevant scaffolding. Context is also crucial, as AI shines when it has context, including system constraints, environment variables, project structure, and more. Creating a Spec or "Grounding" file that outlines the system becomes your source of truth, especially when using AI tools that support file referencing. Using Git as an ideal save system before writing any code helps track thinking over time, create meaningful milestones, and roll back changes if needed. Finally, understanding the tech first is vital to avoid missing subtle mistakes in the output, so it's recommended to use the model to teach yourself what you don't know.
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