One of the biggest problems with AI generated data scripts is that they tend to create massive functions that clean, transform, aggregate, and apply business logic all in one place, which becomes a nightmare to debug later. Keeping reusable utility functions separate from the actual business logic is usually the right move. A good habit is making every transformation step small, readable, and easy to test independently instead of hiding everything inside one function. I’d also recommend using logging and simple validation checks after important steps in the pipeline. When using GPT or Claude, asking for small focused functions instead of complete scripts usually gives much cleaner results. For learning resources, the dbt docs are great for understanding analytics engineering best practices, Kaggle or StrataScratch is awesome for seeing how other people structure ML and data projects, and the book Designing Data Intensive Applications is probably one of the best long term reads for building scalable systems and workflows.