The Rollercoaster Ride of Generative AI Coding

Welcome to my inaugural blog post on the captivating world of generative AI! Like a billion other tech enthusiasts, I’ve been mesmerized by the leaps made by industry leaders like OpenAI and Meta. However, I’m not here to give you a Wikipedia entry on generative AI or wax poetic about its societal implications—maybe another day if the muse strikes. Today, we’re diving into every developer’s favorite generative AI playground: code generation.

Scenario #1: When AI Almost Gets It Right

Picture this: You ask the AI to whip up some code. It looks good—really good. You’re feeling optimistic, so you run it. And… it crashes. Debugging ensues, and you discover subtle errors that you wouldn’t have written yourself. Frustratingly, the AI’s polished output lulled you into a false sense of security. You spend hours unraveling its tangled logic and ultimately conclude, in the immortal words of War Games:

“After very careful consideration, sir, I’ve come to the conclusion that your new defense system sucks.”

General Beringer to Mr. McKittrick

Scenario #2: When AI Surprises You with Brilliance

It’s not all doom and gloom. Let’s say you’re grappling with someone else’s cryptic JSON parsing function (because you obviously always write pristine, well-documented code). You ask the AI to generate test cases for the function, expecting mediocrity. But BAM! Out come beautifully crafted test cases that save you time and headaches. In moments like these, you almost want to hug your laptop.

Lessons Learned: Thriving with Generative AI

After many adventures (and misadventures), here are three pearls of wisdom I’ve picked up:

Lesson #1: Think Small

Stick to generating small blocks of code or individual methods. Smaller chunks mean less room for errors and make debugging manageable. Bonus: This aligns with good software development practices, like keeping functions concise and readable.

Lesson #2: Test Like a Pro

If you’re not already writing tests, start now. Tests are your safety net, letting you confidently evaluate the AI’s work. Whether you’re writing the tests first (TDD) or after the fact, generative AI can assist with either.

Lesson #3: Embrace AI’s Translator Superpowers

Generative AI shines at translating code between languages or styles. Need a Java for-loop converted into a sleek stream-lambda? Done. Want to transform a JSON parser into sample JSON data? Easy. The “transformer” in “transformer model” isn’t just for show.

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Bonus Tip: Refactor Like a Boss

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One unexpected gem: AI excels at reorganizing your code. Whether you’re refactoring a single function or an entire file, the results can be surprisingly elegant. Give it a shot—you might find your new favorite dev assistant.

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Stay tuned for the next post, where I attempt to generate an entire application—front end, back end, and everything in between. Will it be a masterpiece or a monstrosity? You’ll find out soon enough!

Until next time, happy coding!