AI coding became more useful to me when I stopped expecting it to write perfect code on the first attempt.
I started spending more time on the plan. I'd go back and forth, challenge points and work through how the feature should fit into the application. Then I'd let it write the code and review each file afterwards.
That changed the job I was asking it to do. It could handle more of the boilerplate while I stayed involved in the architecture. I kept responsibility for how the application should behave.
I'd tried AI before and hadn't felt it added much to my workflow. Part of trying again was a fairly ordinary fear of being left behind. Other people seemed to be getting more from it, and I wanted to understand what I was missing.
The improvement came from how I worked with it. Planning and reviewing gave me places to apply the experience I'd built over years of writing software. I wasn't just accepting whatever it produced.
That experience matters when you look at the output. You have expectations for the code, and you can tell when a proposed approach needs more thought. The tool doesn't remove those decisions.
For me, this made it easier to move between writing software and other work the business needed. I could agree on the direction, let it produce a version and then come back to inspect it.
The code still needed to meet my expectations. Keeping that review in the process was part of what made the assistance useful.
