Flashcards fail when they are too long, too verbatim, or test more than one fact at a time. A good card isolates one atomic fact and has a cue you can answer without re-reading. Chatbots default to summarizing your notes into multi-fact paragraphs and calling them cards, which is useless for spaced repetition. The 12 prompts below force atomicity, the right export format, and the right test direction. For the full end-to-end workflow, see generating Anki and Quizlet flashcards with AI.
TL;DR
- Paste the prompt that matches your card type, then paste your raw notes where it says
[paste]. - Insist on one fact per card and a short answer (≤30 words). Multi-fact cards become leeches.
- Use the TSV prompt (#7) for a clean Anki import; use the cloze prompt (#2) for fill-in-the-blank sentences.
- Any current model handles this. Claude Sonnet 4.6 and Gemini 3.1 Pro both have a 1M-token context window (as of June 2026), so you can paste a full chapter at once.
- Anki uses the FSRS scheduler by default (since Anki 23.10); keep desired retention near 90% and let it suspend leeches at the default 8 lapses.
Which model to use
These prompts are model-agnostic, but context length matters when you paste long notes:
| Model | Context (as of Jun 2026) | Notes |
|---|---|---|
| Claude Sonnet 4.6 | 1M tokens | Strong at terse, atomic phrasing; good default |
| Gemini 3.1 Pro | 1M tokens | Free tier in Google AI Pro ($19.99/mo); handles whole textbooks |
| GPT-5.5 (ChatGPT) | ~320 pages in-app on Plus ($20/mo); full 1M only on $200 Pro | Fine for chapter-sized batches |
For most study sessions, the free tier of any of these is enough. Paste one chapter or lecture at a time and review the output before importing.
Best for
- Language learning (vocab + grammar)
- Medical and law school
- Coding interview prep
- New-tech onboarding (frameworks, APIs)
- Exam cramming with high-yield material
1. Notes → atomic cards
From the notes below, generate 20 atomic flashcards. Each card: 1 question, 1 short answer (<=30 words). Skip anything that requires more than one fact to answer. If a sentence has 3 facts, make 3 cards. Output as Q: / A: pairs.
[paste]2. Cloze-deletion cards
Convert each sentence below into a cloze-deletion card using Anki syntax. Hide the key term with {{c1::term}}. If a sentence has 2 testable terms, use {{c1::}} and {{c2::}} on the same card so it makes two cards. Do not nest more than 3 levels deep. Output one card per line.
[paste]Paste the result into an Anki Cloze note type (the Text field must use the cloze: filter, which the built-in Cloze type already does).
3. Language vocab cards
I'm learning [language]. From this vocab list, generate cards: front = word in [language], back = (1) 1 translation, (2) 1 short example sentence in [language], (3) part of speech, (4) gender or tone marker if relevant. Skip cognates that are obvious to an English speaker.
[paste]4. Code-snippet cards
From these code patterns I want to memorize, generate cards: front = a 1-sentence description of when to use this pattern (the trigger), back = the snippet plus a 1-line caveat. Avoid making cards where the front is just the function name.
[paste]5. Image-occlusion alternative
For a diagram of [system], generate 10 text-only cards each isolating one labeled component. Format: front = "In the [system], what is the role of the component that [short description]?", back = name + function <=25 words.6. Reverse-direction pair cards
For each card below, generate the reverse direction (if Q is "what is X", make a card "what's an example of X"). Mark which direction is harder for a beginner. Skip reverse cards for facts where the reverse has no useful retrieval ("what year was 1969?" reversed is useless).
[paste cards]7. Anki import format (TSV)
Output 15 cards on [topic] in Anki TSV format: front<TAB>back<TAB>tags. Use 2-3 tags per card. First tag = subject, second = subtopic, third (optional) = difficulty (easy/medium/hard). One card per line, no surrounding markdown.Save the output as a .txt file, then File → Import in Anki, set the field separator to Tab, and map column 3 to Tags.
8. Spaced-review priority cards
From my last quiz, I got these wrong (paste). Generate review cards targeting the specific gap each wrong answer reveals, plus 2 "prerequisite" cards for any missing foundation. For each card, note which quiz question it traces back to.
[paste]9. Definition + example + non-example
For [topic], generate 10 cards where each card has 3 parts on the back: (1) the definition, (2) one clear example, (3) one non-example that looks like it should qualify but doesn't. Front: just the term.10. Compare-and-contrast cards
For these paired concepts I keep confusing, generate compare-and-contrast cards: front = "What's the key difference between [A] and [B]?", back = one diagnostic feature that separates them + the situation where each applies.
[pairs]11. Process / sequence cards
For this multi-step process, generate ordered cards: 1 card per step. Front = "In the [process], what comes after [step N-1]?". Back = step N + 1-line reason it has to come there. Plus 1 summary card listing all steps in order.
[paste process]12. Leech-prevention rewrite
These 5 cards I've failed 4+ times in Anki. Rewrite each one to fix the leech: the cue is probably too vague, the answer too long, or the card tests two facts. Output the rewritten card plus a 1-line diagnosis of what was broken.
[paste leeches]In Anki a card is auto-tagged a leech after 8 lapses by default (lower it to 4 in Deck Options if you want earlier intervention). With FSRS, suspending leeches and rewriting them keeps your true retention near the 90% target instead of letting one bad card eat your review time.
Common mistakes
- Multi-fact cards that test more than one thing — leeches in waiting.
- Verbatim copy from notes with no atomic isolation.
- No reverse direction for paired concepts that need both retrieval paths.
- Front too long — if the cue takes 10 seconds to read, retrieval already failed.
- Cards on facts you don’t need — bloat kills review velocity. Pushing FSRS retention from 90% to 95% roughly doubles your daily reviews, so every junk card costs you twice.
FAQ
Which AI model makes the best flashcards in 2026? All current top models (Claude Sonnet 4.6, Gemini 3.1 Pro, GPT-5.5) produce good cards from these prompts. The differentiator is context length: Sonnet 4.6 and Gemini 3.1 Pro both hold 1M tokens, so you can paste a full chapter; ChatGPT Plus handles roughly 320 pages in-app. Quality of phrasing comes from the prompt, not the model.
Does the cloze prompt (#2) work with the current Anki?
Yes. The {{c1::term}} syntax is unchanged in Anki 24.11, and the built-in Cloze note type already applies the cloze: filter to its Text field. The only new limit worth knowing is that nesting clozes deeper than 3 levels is not supported.
How do I get the cards into Anki without manual retyping?
Use prompt #7 to get tab-separated values, save them as a .txt file, then File → Import with the separator set to Tab. Map the last column to the Tags field. For cloze cards from #2, paste directly into a Cloze note or import as TSV into a Cloze-type deck.
Should I lower the leech threshold? The default is 8 lapses. If a card is wrong that many times, the card is broken, not your memory. Lowering it to 4 surfaces problem cards sooner so you can rewrite them with prompt #12. Auto-suspend leeches so they stop draining your daily queue.
Can AI also schedule my reviews? No, and it shouldn’t try. Let FSRS handle scheduling (it has been Anki’s default since 23.10) and keep desired retention around 90%. Use AI only to author and repair cards — see how to generate Anki and Quizlet flashcards with AI for the full pipeline.
Related
- Quiz generation prompts
- Language learning prompts
- How to Generate Anki & Quizlet Flashcards With AI
- Study plan prompts
- Anki FSRS deck options (official manual)
Tags: #Prompt #Study #Flashcards