> But, on the flip side, I personally advocate hard for AI from the point-of-view on accessibility. I know (more-or-less) exactly what output I'm aiming for and control that obsessively, but it's AI and my voice at the helm instead of my fingertips.
This is the technique I've picked up and got the most from over the past few months. I don't give it hard, high-level problems and then review a giant set of changes to figure it out. I give it the technical solution I was already going to implement anyway, and then have it generate the code I otherwise would have written.
It cuts back dramatically on the review fatigue because I already know exactly what I'm expecting to see, so my reviews are primarily focused on the deviations from that.
The only issue to beat in mind is that visual inspection is only about 85% accurate at its limit. I was responsible for incoming inspection at a medical device factory and visual inspection was the least reliable test for components that couldn’t be inspected for anything else. We always preferred to use machines (likes big CMM) where possible.
I also use LLM assistance, and I love it because it helps my ADHD brain get stuff done, but I definitely miss stuff that I wouldn’t miss by myself. It’s usually fairly simple mistakes to fix later but I still miss them initially.
I’ve been having luck with LLM reviewers though.
This, and I curate a tree of MD docs per topic to define the expected structure. It is supposed to output code that looks exactly like my code. If not, I manually edit it and perhaps update the docs.
This is how I've found myself to be productive with the tools, or since productivity is hard to measure, at least it's still a fun way to work. I do not need to type everything but I want a very exact outcome nonetheless.