This is very close to how I often use LLMs [0]. A first step in deciphering code where I otherwise would need to, to use the authors words, power through reading the code myself.
It has been incredibly liberating to just feed it a spaghetti mess, ask to detangle it in a more readable way and go from there.
As the author also discovered, LLMs will sometimes miss some details, but that is alright as I will be catching those myself.
Another use case is when I understand what the code does, but can't quite wrap my head around why it is done in that specific way. Specifically, where the author of the code is no longer with the company. I will then simply put the method in the LLM chat, explain what it does, and just ask it why some things might be done in a specific way.
Again, it isn't always perfect, but more often than not it comes with explanations that actually make sense, hold up under scrutiny and give me new insights. It actually has prevented me once or twice from refactoring something in a way that would have caught me headaches down the line.
[0] chatGPT and more recently openwebUI as a front end to various other models (Claude variants mostly) to see the differences. Also allows for some fun concepts of having different models review each others answers.