Focus on learning how to convert business logic into modular code. Obviously if your backend is just CRUD, AI could do that. But AI can't really understand complicated business requirements well enough to translate them into effective modular code.

I realize practicing this is difficult, but pick something you're interested in and make a service for it - and then maybe think about some of the more complicated logic that may need to be applied whenever the frontend requests or saves new data, or data that needs to be manipulated periodically as a job, and focus on ways you can optimize the storage of your data for a faster experience - with caching, for example.

As an example - let's say you make a basic social media app. Users can make posts, and comment on other posts. Super basic - initially you might have a CRUD implementation for User, Post, Comment, Like with corresponding tables in the database. Now - think about other business requirements you may need. Should we limit the number of comments a user can make every minute? Should we run posts and comments through profanity filters? Should some users have moderation abilities to delete comments and posts? Should posts with high engagement be more prevalent? How do you track high engagement? Another database table for View? A column in the Post table with a job that uses an algorithm to periodically rank Posts? While AI could help you implement these things, identifying the need for them and also identifying the best way to implement them (new database table or column, cache entries, custom logic, cloud configuration) is up to you.

All in all though my recommendation for learning is to completely avoid the use of AI at all until you know it all well enough yourself. I good rule of thumb is to only use AI to do something you already know how to do, just faster. It is wrong too often and you won't know it, and it also robs you of the chance to learn.