How I Use AI Tools in My Daily Dev Workflow

I don’t treat AI tools as a magic button that writes my code for me. Instead, I think of them as teammates that sit next to me in the editor, helping me move faster, stay unblocked, and keep a higher level of focus on architecture and product decisions.

My main hub is Cursor. I use it as my editor, but the real power comes from having AI deeply integrated with the codebase. I’ll start a feature by writing a plain‑language plan in a comment or in a scratch file—what I want to build, edge cases to cover, and how it should feel in the UI. Then I ask Cursor to scaffold components, routes, or API handlers based on that plan. I don’t accept everything blindly; I review, refactor, and shape the code so it matches my style and standards.

For more complex reasoning—like designing data models, breaking down big refactors, or exploring alternative architectures—I lean on tools like Claude Code and GPT-based assistants. I’ll paste in relevant parts of the codebase and ask questions like “what’s the safest way to introduce this without breaking existing behavior?” or “how would you structure this module so it’s easier to test?”. The goal isn’t just to get an answer, but to pressure-test my own ideas and see tradeoffs I might miss when I’m too deep in the code.

Cursor CLI is something I started using to escape living entirely inside the Cursor IDE. It lets me keep the AI context and assistance I like from Cursor, while doing the actual editing in VS Code or whatever IDE I feel like using that day. I stay in the terminal, run my usual workflows, and still have access to AI help without being locked into a single editor.

Day to day, AI also helps with the “glue work” that usually slows developers down: writing commit messages that actually explain the why, drafting documentation or README updates right after a feature ships, summarizing big diffs, and turning vague ideas into concrete todos. I try to keep a tight feedback loop: describe what I’m doing, get a suggestion, adapt it, run the tests, and repeat.

The most important rule I have is that I stay in control of the codebase. AI can propose implementations, but I’m responsible for the final design, security, and performance. Used this way, tools like Cursor, Claude Code, and assistants like this one don’t replace the craft of software engineering—they free up more mental space so I can focus on building better products and shipping faster.