Spaced repetition systems have gotten better

My personal workflow with memorizing with Anki using LLMs is as follows: Read textbook material first. It's important that you understand what you are learning.You cannot just breakdown information into atoms and expect to understand how it works together (for example: you can learn that there are monounsaturated fats, polyunsaturated fats, saturated fats and trans fats. But without reading beforehand about them from a textbook or other source, you will not understand how they differ (in chemical structure, biological function etc). After I understand the material, I feed LLM the documents (textbooks etc) and give it the following prompt: >I want to generate flashcards from the provided textbook document using the attached PDF. Each flashcard should contain a question and a corresponding answer formatted as pairs in plaintext code block. The structure should be: "Question","Answer" >Extract key concepts, definitions, and explanations from the textbook. If in the text imperial unit system is used, convert it to metric. For mathematical symbols and equations, format them using inline MathJax syntax because I will be importing the copied text to Anki. Ensure questions are clear and concise while answers provide a direct yet comprehensive response.

After the text is generated, I check out the accuracy (in 95% of cases the cards are accurate) and I import them into my decks. The rest is good old school Anki memorizing.

I remember trying that a few months back but I was not convinced by the quality of the cards. It's also hard to convey what I think a good card is, I guess there's something about not getting lost in the details while missing the big picture.

You only commented on accuracy, but what's your experience on relevance and how useful LLM-generated flashcards are?

To be clear I've already found myself deleting some flashcards I made myself while reviewing them when I realized they were bad, so I guess one can do that for LLM-generated questions as well, as long as the irrelevance rate is somewhat similar.

Well I can give you an output example. This was generated based on chapter from textbook about Vitamin (Vitamin D in this example)

"Which nutrient can be synthesized by the body using sunlight?","Vitamin D" "What is the primary role of vitamin D in calcium regulation?","Raises blood calcium by enhancing absorption, mobilizing bone stores, and reducing kidney excretion" "How does vitamin D affect bones?","Supports bone mineralization and integrity" "What form does vitamin D take before activation?","Inactive precursor synthesized in the skin or consumed in diet" "Which organs activate vitamin D?","Liver and kidneys" "What are signs of vitamin D deficiency in children?","Bowed legs and bone deformities (rickets)" "What is osteomalacia?","Soft, weak bones in adults due to vitamin D deficiency" "What disease is caused by long-term vitamin D deficiency in adults?","Osteoporosis" "How does vitamin D deficiency affect older people?","Increases risk of fractures and joint pain" "What is the toxic effect of too much vitamin D?","Calcification of soft tissues"

You get the idea. How would you rate it's usefulness is subjective but it gets the job done.

I have been looking for a workflow to do exactly this!

Which LLM do you use?

Do you do anything special to structure the deck by chapter or section?

Yes I use tags and then I display the tag using CSS at the bottom of each question. You can find out how to do this from anki manual. EDIT: this is useful because sometimes different chapters will contain similar questions but with the focus on different answer (for example: when studying trigonometry and when studying derivative of a function, similar questions will be present so you will need tags to guide you towards the answer).