▲ 0 r/Anki
Disclosure: I'm building a tool in this space (Notella). I should have led with this and didn't — apologies. Wrote up the honest version of what AI cards get right and wrong here: https://notella.app/blog/ai-flashcards-from-lectures
Rest of the post is my actual experience trying to move the card-creation bottleneck. Genuinely interested in what people in this sub do, but if mods think this crosses the line I understand and will take it down.
I record dense lectures (pharm-heavy, lots of facts to memorize) and the time it takes to turn them into proper Anki cards has always been the bottleneck. 90 minutes of audio turns into 2-3 hours of typing if I do it right. I lose Wednesday and Thursday to it before the next batch starts.
Spent the last few months testing different AI generators to see if I could move that bottleneck. The results were mixed enough that I figured this sub would have stronger opinions.
What worked well:
- Definitions, drug-class associations, simple mechanism cards. "Define afterload," "What does metformin inhibit?" — generated cards were usable as-is, sometimes better than what I'd write tired at 11pm.
- Cloze deletions for compound facts. Most generators handle these better than Q&A formats.
What didn't work:
- Integration questions. If a lecture builds a 20-minute argument (this presentation suggests this diagnosis because of these three findings), AI breaks it into atoms and loses the synthesis. You get cards on each fact but no card that tests the reasoning. Have to add those by hand.
- High-yield filtering. The model has no idea what your exam blueprint is. It happily generates a card on a passing comment about a 1962 study with the same weight as the actual high-yield mechanism. Editing pass to delete trivia is non-negotiable — typically 20-30% of generated cards get cut.
- Hedge words. The model sometimes interprets "usually around" or "often presents with" as definite claims. A few generated cards end up confidently wrong, so verify anything load-bearing (drug doses especially).
Workflow that ended up sticking:
1. Generate from transcript (5 min)
2. Edit pass: skim every card, delete trivia, tighten verbose stems, add 5-10 integration cards by hand (15-20 min)
3. Tag and import to Anki
Down from 2-3 hours of typing to ~25 min total. Anki itself stays the SR engine — none of these tools are trying to replace the algorithm or your add-ons.
Anyone else running AI generation alongside Anki? What's your editing checklist? Anything that's bitten you that I should be looking out for?
u/Aggravating_Slice870 — 8 days ago