I've been working with a data processing pipeline that was vibe-coded by an AI engineer, and while the code works, as software that has to fit into a production

I've been working with a data processing pipeline that was vibe-coded by an AI engineer, and while the code works, as software that has to fit into a production environment, it's a mess. Take logging for example. The pipeline is made up of AWS lambdas written in python. The person who built it wanted to add context to each log for debugging and the LLM generated hundreds of lines of python in each lambda to do this (no common library). But he (and the LLM) didn't understand that there were a bunch of files that initialized their own loggers at the top of the file, so all that code to set context in the root logger wouldn't get used in those files. And then he wanted to parallelize some tasks, and both he and the LLM didn't understand that the logging context was thread-local and wouldn't show up in logs generated in another thread. So what we ended up with was 250+ line logging_config.py files in each individual lambda that were only used for a small portion of the logs generated by the application.