It's also why I urge junior engineers to not rely on AI so much because even though it makes writing code so much faster, it prevents them from learning the quirks of the codebase and eventually they'll lose the ability to write code on their own.
I am afraid, the cat is out the bag, and there is no turning back with GenAI and coding – juniors have got a taste of GenAI assisted coding and will persevere. The best we can do it educate them on how to use it correctly and responsibly.
The approach I have taken involves small group huddles where we talk to each other as equals, and where I emphasise the importance of understanding the problem space, the importance of the depth and breadth of knowledge, i.e. going across the problem domain – as opposed to focusing on a narrow part of it. I do not discourage the junior engineers from using GenAI, but I stress the liability factor and the cost: «if you use GenAI to write code, and the code falls apart in production, you will have a hard time supporting it if you do not understand the generated code, so choose your options wisely». I also highlight the importance of simplicity over complexity of the design and implementation, and that simplicity is hard, although it is something we should strive for as an aspiration and as a delivery target.
I reflect on and adjust the approach based on new observations, feedback loop (commits) and other indirect signs and metrics – this area is still new, and the GenAI assisted coding framework is still fledging.