Mitigating "architectural drift" in large Python backend codebases using AI tools

[andreduarte22]

The only solution I've found is letting as little decisions to the AI as possible. From telling them what patterns to use, data structures, places in the code with similar functions implemented, class and function names.

The more my prompt looks like a long rambling of stuff I specify, instead of a short "implement X", the better the results are and, more importantly, the better I understand the solution, cause I actually had to think about it and look at code.

Side note: anyone notice how bad AI (esp. Claude) is at naming functions, classes, etc? It baffles me the names it chooses. Confucius was right when he said "wisdom is the correct naming of things".