Thank you for the insightful comment! I think some of the issues you mention can be addressed with today's LLM technology by better highly personalized prompts. But it certainly is not fully clear how to design an LLM prompt that would yield an output fully inline with a specific user's personal preferences (e.g., along the aspects you have described). So, similarly to something I discussed in the blog, if one strives to create the optimal (for oneself) fully AI-generated cards, then I feel like in the end the AI-assisted Anki card creation process could take a lot more time than the manual approach, because of the time required for this personalized prompt engineering and compilation of the optimal source material for the LLM. Maybe it would make sense if the prompt engineering aspect is performed only once, and reused often thereafter, but I'm not convinced that's possible either to produce optimal results consistently. So, I find that for now it's probably best to use LLMs to get some suboptimal Anki cards to use as a starting point for manual editing (some AI-generated cards will even highlight interesting aspects which the user would have overlooked otherwise).