One of the biggest changes I've experienced in software development recently is how AI changes the process of building software.
At first, AI coding feels like:
"Give me the code."
But that isn't where the real productivity comes from.
The bigger advantage is being able to use AI throughout the development lifecycle:
🧠 Brainstorming architecture
📝 Breaking requirements into tasks
💻 Generating boilerplate
🔍 Reviewing and refactoring code
🐛 Debugging difficult issues
🧪 Creating tests
📚 Understanding unfamiliar codebases
⚙️ Automating repetitive workflows
The developer still needs to understand why something should be built and whether the generated solution is actually good.
AI can produce code incredibly quickly.
But it doesn't automatically produce a good architecture.
That's where engineering experience still matters.
My current approach
Instead of asking AI to build an entire application blindly, I prefer to break the project into smaller milestones.
Requirement → Architecture → Implementation → Review → Testing → Iteration
AI becomes a development partner inside that workflow.
The goal isn't to replace software engineering.
The goal is to remove unnecessary friction so developers can spend more time solving the problems that actually matter.
I'm still experimenting with this approach, and I'll be sharing what works—and what doesn't—as I build more AI-powered products.
How are you currently using AI in your development workflow?
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