Building Your AI Coding Workflow
Now that you understand AI coding tools, it's time to build a sustainable workflow. The goal is using AI as a powerful assistant while ensuring you actually learn and grow as a developer.
A Balanced Workflow
Here's a workflow that balances productivity with learning:
1. Plan — Before touching AI, think about what you need to build. What are the inputs? Outputs? Edge cases? This mental work is yours, not AI's.
2. Draft — Ask AI for an initial implementation. Use specific prompts that describe your requirements clearly.
3. Review — Read through the generated code. Do you understand every line? If not, ask AI to explain the parts that confuse you.
4. Test — Run the code with various inputs. Don't assume it works because AI said so.
5. Refine — Iterate with AI or make manual fixes until the code meets your needs.
6. Learn — Ask AI to explain concepts you encountered. Turn each coding session into a learning opportunity.
AI as Pair Programmer
Think of AI as a knowledgeable colleague sitting next to you. You wouldn't let a colleague write all your code while you watch — you'd collaborate. Similarly:
- You decide what to build and how it should work
- AI helps with implementation details and syntax
- You verify that the result is correct
- You learn from the process
Balancing Learning and Productivity
When you're learning, resist the urge to let AI do everything. If you're practicing loops, write them yourself first, then compare with AI's version. The struggle is where learning happens.
When you're building something and already understand the concepts, let AI handle boilerplate so you can focus on the interesting parts.
The Golden Rule
Always understand what your code does.
If you can't explain your code to someone else, you don't really own it. You'll struggle to debug it, extend it, or learn from it. AI is a tool to help you write better code faster — not a replacement for understanding.