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How an AI flashcard maker decides what goes on a card

What separates a good card from a bad one

A card is only useful if it tests one thing and can be answered without seeing the source. That sounds obvious and it is where most automatic generation fails: turning every sentence into a question produces cards like “What did the chapter introduce?”, which cannot be answered and cannot be marked. Laxu AI generates 22 to 40 cards per upload rather than one per paragraph, because the count follows the number of genuinely distinct testable facts in the material. A 40-page chapter with eight definitions produces eight definition cards, not forty. Each card carries a question, an answer, a hint, and an explanation, so a card you get wrong tells you why rather than only that you missed it. Cards are also written to stand alone, so they still make sense weeks later when you have forgotten which upload they came from.

What you can upload, and what each format is good for

PDFs are the strongest input, because the text is already machine-readable and the structure survives — headings stay headings, so a chapter becomes a coherent card set. Images work well for textbook pages and printed slides, and acceptably for neat handwriting. Audio suits lectures and podcasts, where a 50-minute recording of roughly 7,500 spoken words condenses to the same 22 to 40 cards. Pasted text is the most predictable of all, since nothing is being extracted or transcribed before the cards are written. Most uploads finish in under two minutes. You can also create cards by hand with no limit, which remains the right answer for dense notation. Mixing formats is fine — a chapter PDF and the lecture recording that covered it can be uploaded separately and studied as one subject.

Why AI-generated cards still need editing

Generated cards are a first draft that is right most of the time, and the exceptions cluster predictably. Anything the source stated ambiguously stays ambiguous on the card. Proper names and formulas carry over any error made while reading a photograph or transcribing audio. And a card occasionally tests a fact that is true but not what your course actually examines. Every card is editable, and the fastest habit is to review the set once immediately after generation rather than discovering a bad card three weeks later mid-revision. Deleting five irrelevant cards takes under a minute; unlearning a wrong one takes considerably longer. The same applies to the notes and quiz generated alongside them, which are editable for the same reason.

Where the cards go afterwards

Cards are studied inside Laxu AI with spaced repetition, so material you get wrong returns sooner and material you know recedes. The same upload also produces structured notes and a practice quiz from the identical source, which means the notes and the cards never disagree with each other. If you already run a review system elsewhere, Pro exports to Anki and to PDF, so the generation step can sit in front of a workflow you have already built rather than replacing it. Everything syncs across web, iOS and Android, and the interface is available in nine languages on mobile. Material generated in one language stays in that language, so a Spanish lecture produces Spanish cards regardless of your interface setting.

Ways to make flashcards from study material, ranked

1. Generate from the source, then edit. Fastest route to a usable set, and editing a draft is far quicker than writing from nothing. Laxu AI is free for one upload; Pro is $4.99/month or $39.99/year.
2. Write cards by hand as you read. Slowest, and still the best for learning — the act of deciding what deserves a card is itself revision. Free and unlimited in Laxu AI.
3. Download someone else’s shared deck. Instant, but the cards match their syllabus rather than yours, and you inherit their mistakes without knowing where they are.
4. Convert an existing quiz or question bank. Good when the questions are already exam-shaped, though it only covers material someone has already written questions for.

Common questions