It might be 1), being an early adopter doesn’t help much with AI. So much is changing constantly. If you put a good description of your architecture and coding guidelines in the right .md files and work on your prompts the output should be much better. In the other hand your project being legacy code probably also doesn’t help.
We find across our team different people are able to use these things at different levels. Unsurprisingly, more senior coders with both more experience in general and more experience in ai coding are able to do more with ai and get more ambitious things done more quickly.
A bummer is that we have a genai team (louie.ai) and a gpu/viz/graph analytics team (graphistry), and those who have spent the last 2-3 years doing genai daily have a higher uptake rate here than those who aren't. I wouldn't say team 1 is better than team 2 in general: these are tools, and different people have different engineering skill and ai coding skill, including different amounts of time doing both.
What was a revelation for me personally was taking 1-2mo early in claude code's release was to go full cold turkey on manual coding, similar to getting immersed in a foreign language. That forced eliminating a lot of bad habits wrt effective ai coding both personally and in state of our repo tooling. Since then, it's been steady work to accelerate and smooth that loop, eg, moving from vibe coding/engineering to now more eval-driven ai coding loops: https://media.ccc.de/v/39c3-breaking-bots-cheating-at-blue-t... . That takes a LOT of buildout.
I don't have any links but you can start with CLAUDE.md and/or AGENTS.md and put the basic instructions in there ( you can also google these filenames for examples and recommendations). I also put README.md's in every subfolder to describe which file does what, etc.
