My Journey Through AI & Developer Productivity: Lessons Learned

A few years ago, I dreaded opening up my code editor. Not because I didn’t love what I do—I live for great abstractions and brutal bug hunts—but because it felt like I was stuck on a productivity treadmill. Always sprinting, never gaining ground. That all changed when I started folding AI into my dev workflow. What started as casual curiosity quickly became career-altering. So, here’s the story of that shift—AI tools I used, mistakes I made, and what actually moved the needle on productivity.

My Early AI Experiments (a.k.a. the Copy-Pasta Era)

Like many devs, my first real interaction with AI in programming was using GitHub Copilot. At first, I treated it like a kind-hearted code genie—"Autocomplete this function for me," I'd whisper. And it would. Often surprisingly well.

But I learned pretty quickly: using AI without a workflow is like throwing rocket fuel into a lawn mower. It's fun for a second. Then it's chaos.

AI Isn’t Just a Code Generator—It’s a Force Multiplier

Once I got past using AI tools just for “cheat mode code suggestions,” I started putting them to work across the coding lifecycle:

• **Planning:** Tools like Notion AI help me break down large feature specs into digestible technical tasks. That used to take hours. Now? Minutes.

• **Refactoring:** I use AI pair programmers like CodeWhisperer to suggest cleaner versions of nasty logic I wrote during a caffeine-fueled 1am sprint.

• **Debugging:** Tools like Phind (an AI coding search engine) help me troubleshoot obscure errors 10x faster than digging through decade-old Stack Overflow threads.

The biggest shift? I now think of AI not as a replacement—but as another team member who never sleeps, never judges, and occasionally writes flawless TypeScript.

Automation: Small Scripts, Big Wins

A surprising productivity boost came not from flashy tools, but from automating repetitive tasks. AI helped write shell scripts, Git hooks, and cleanup utilities I’d otherwise procrastinate forever. Here are a few favorites:

• A script to generate changelogs automatically from PR titles using OpenAI’s GPT-4

• A CLI tool that guesses which test files are affected based on diff metadata, saving hours on CI checks

• ChatGPT prompts that convert pseudocode into working Python scripts in seconds

What I Learned (the Hard Way)

AI can make you faster, but if you’re going in the wrong direction, it’ll just get you lost more efficiently. Trust, but verify. Here are key lessons I had to learn, sometimes painfully:

1. Always review AI-suggested code—don’t let it scare you into shipping nonsense.

2. Customize your prompts. Generic prompts get generic answers. Good prompts are like good commits: focused and descriptive.

3. Don’t chase shiny tools—integrate purposefully into your stack, one win at a time.

Final Advice: Think Systems, Not Just Tools

If there’s one thing I’d pass on to a fellow dev: make AI part of your system, not a side project. Combine tools like Copilot, GPT, and automation scripts with source control, testing habits, and documentation flows. That’s when productivity doesn’t just improve—it compounds.

And hey, if you're still clinging to your old workflows like a favorite hoodie with holes in it—I get that too. But I promise: once you’ve optimized your dev flow with AI, you’ll wonder how you ever shipped code without it.