How Many Anki Cards per Lecture? Count Reviews, Not Cards

Every card you make today is a card you owe tomorrow, again a few days later, and again the week after. A new card shows up several times in its first month before the intervals stretch out. So the cost of a card is not one review. It is a short tail of reviews, and that tail overlaps with the tails of every other card you made that week.

Put your numbers in. Forty cards a lecture at ten lectures a week is four hundred new cards a week. Each of those returns several times over the following weeks, so by the third week you are answering this week's four hundred plus the returning tails of the previous eight hundred. That is where the "three hours a day just doing reviews" stories come from, and where the 7,000-overdue backlog comes from when someone takes two weeks off. Surviving a review backlog covers the recovery. This page is about not getting there.

Now run it backwards. Decide how many reviews you can do on a normal weekday, not a heroic one. Divide by roughly the number of times a card returns in its first few weeks. That gives a weekly new-card budget. Divide by lectures per week and you have your number. It is personal, it depends on your schedule, and it is the only number that matters.

The most useful reply in that thread was a question to ask yourself before opening the card editor: "If I could only make 10 cards for this lecture, which ones would they be?" Make those ten. Then, if the budget has room, ask which five you would add next. The order matters because the first ten are chosen by importance and the next twenty are usually chosen by whatever was on the slide.

Back to the 23,000 cards. When another student asked how that happened, the answer was honest: it came from impostor syndrome and a wish to "prove" themselves, and it did not come from a healthy place. That is worth sitting with, because a card count is a scoreboard. It is visible, it compares across people, and it feels like effort. Reviewing is invisible and compares to nothing.

Some signs the count has become the goal. You feel worse about skipping a slide than about skipping a review session. You know your card total and not your retention. You make cards for lectures you will never review because the block ends next week. You read about 1,000-card days and 15,000-review queues and feel behind, even though those numbers describe someone else's exam. The workflow side of this, the evening that disappears into the card editor, has its own page: when making cards takes too long.

A card should test one relationship you can retrieve and grade in a few seconds. A six-step mechanism is not one card, and it should not be six cards either. The mechanism belongs in the understanding layer: your notes, a pathway you drew, or a written walkthrough of the lecture. Then one or two cards test the endpoint. "Why does X cause Y?" with a short answer, not the whole pathway on the back.

Say you have 600 cards from this block and a budget for 80 reviews a day. Do not delete anything. Deleting loses the work and the scheduling history, and you will want some of those cards back before the final.

If you upload a lecture to SynapticRecall, the generator is instructed to prioritise learning objectives, repeated emphasis and anything the professor checked with an in-lecture question, and to stay under roughly three cards per content slide. Each card carries a short note on why it matters and the source slide image on its back. So a forty-slide lecture comes back as a bounded deck rather than a card for every bullet point. The full flow is on PDF to Anki.

You still delete. Three per slide is a ceiling, not your number. Open the deck against the slides, ask the ten-card question, and suspend what does not survive it. On the Standard plan and above you can edit cards in the library and re-download the deck. And check the output: the generator can miss a detail or weight the wrong slide, so the ten-minute audit still applies before you import.