Toeing the Line Between High Productivity and Quality Production
from trixtur
We’ve all been there: you catch a spark of passion for a project and start churning out work fast. Often that yields a lot of code, but not a lot of good code. Add AI agents to the mix and now you can generate a lot of garbage code—and even “test” all of it.
High output is great; it makes the bottom line look good and impresses management. But the praise evaporates when it deploys and something breaks—or worse, when it’s a pile of code that does nothing.
Just because code looks good and appears to do what you want doesn’t mean it’s actually good. So how do you toe the line?
Early on with AI, I assumed “include tests” would solve it. If there are tests, it works, right? Nope. Seriously, nope. I even had the agent create simulations; that helped, but still no dice.
When the rubber meets the road, you need to set up an environment and run the code. Run the simulation manually. That human touch—actually exercising the thing—makes the difference between fully automated text and software that truly works.
We’re headed toward a future of code generation plus manual validation. Our work will drift more into thinking and writing—not writing in the traditional sense, but prompting and observing. The time spent conceptualizing and making deliberate, iterative changes will be what separates generated slop from quality production.