---
id: jp-learning-and-anki-tooling/04-deck-hygiene-leeches-and-sentence-cued-cards
title: 'Project status: Japanese immersion learning with Anki and selective glossing:
  4. Deck hygiene: leeches and sentence-cued cards'
report: https://ai.hiina.space/reports/jp-learning-and-anki-tooling.md
section: 4
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# Project status: Japanese immersion learning with Anki and selective glossing: 4. Deck hygiene: leeches and sentence-cued cards

- Section 4 of 12 of the report "Project status: Japanese immersion learning with Anki and selective glossing" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/jp-learning-and-anki-tooling.md
- Previous: https://ai.hiina.space/reports/jp-learning-and-anki-tooling/03-scheduler-settings-fsrs-6-on-anki-26.md
- Next: https://ai.hiina.space/reports/jp-learning-and-anki-tooling/05-mining-from-more-anime.md

## 4. Deck hygiene: leeches and sentence-cued cards

### Leeches ("rewrite the leech cards so they're less confusing")

Set the leech threshold to 6 with action Tag Only, as vnclub recommends. Cards then join a `tag:leech` rewrite queue instead of disappearing into suspension. Anki's default is 8 lapses with tag plus suspend ([Anki manual: Leeches](https://docs.ankiweb.net/leeches.html)). The queue is worked with a four-way triage. The taxonomy is a synthesis, but each branch follows the manual or forum advice:

1. **Interference with a similar word or kanji.** Suspend one of the pair until the other is solid, or build a contrast card that shows both and focuses on the difference ([forum thread](https://forums.ankiweb.net/t/how-do-i-deal-with-pairs-of-cards-that-i-confuse-for-each-other/55515)).
2. **Overloaded card.** Several senses or a long definition. Shrink Lapis's MainDefinition to the sense actually met, and push the rest into Glossary.
3. **Sentence-cued recall.** Handled by the sentence-cue fix below.
4. **Low-value word.** Delete it.

When a rewrite changes the cue substantially, the card is Reset. Reset keeps review history, so the FSRS optimiser still sees reviews of the old cue ([Anki manual: Studying](https://docs.ankiweb.net/studying.html)). That is a mild contamination risk whose size nobody has verified.

For mnemonics on stubborn leeches, the companion memory-palace report supplies a constraint. Cues learners make themselves beat other-generated cues by a wide margin in lab studies (Mäntylä, via [Wheeler & Gabbert 2017](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.01830/pdf)). A 30-student pilot also found that AI-generated associations far from the learner's own thinking can interfere with memory ([J-STAGE](https://www.jstage.jst.go.jp/article/his/27/4/27_261/_article/-char/en)). So in this project an LLM may propose a mnemonic or a contrast card, but the learner edits and chooses it.

### Sentence-cued cards ("swap out the example sentences so cards cued by the sentence get reset")

This to-do has the best evidence behind it. A 2024 PNAS paper (six experiments on foreign vocabulary) found that varying the sentence cue across retrieval cycles beat a constant cue, immediately and after 24 hours, and more so when practice was spaced. Learners nonetheless judged constant cues more effective ([PNAS via SWPS](https://share.swps.edu.pl/handle/swps/1120)). That metacognitive illusion explains why sentence-cued cards feel fine until the word turns up somewhere else.

Lapis has four card types (word plus sentence, Click, sentence, audio) but no sentence rotation, and no add-on that rotates sentences per note turned up ([Lapis README](https://github.com/donkuri/lapis)). That is absence in a limited search. The closest existing tool, anki-ai-dynamic-cards, generates a fresh LLM sentence at review time but supports English only and stores nothing ([GitHub](https://github.com/AleksandrFurmenkovOfficial/anki-ai-dynamic-cards)).

The manual sequence planned now:

1. **Find the sentence-cued cards.** Build a filtered deck of mature Lapis cards, temporarily flip them to a word-only front, and see which ones fail. Those are the sentence-cued ones (inference; this is a self-test, not a published method). This is the probe used in the next gate.
2. **Switch every mined note to Click cards** (`IsClickCard`), which hide the sentence until clicked, as Donkuri recommends ([Donkuri mining](https://donkuri.github.io/learn-japanese/mining/)).
3. **Reset only the probe failures.** Leave the passes alone so their stability survives.

Real rotation needs a custom template: an extra field holding several sentences, with JavaScript picking one at render time. Anki templates support JS, and a forum script already shuffles content on a card front ([forum thread](https://forums.ankiweb.net/t/how-do-i-deal-with-pairs-of-cards-that-i-confuse-for-each-other/55515)).

The extra sentences should come from real text, not free generation. The one Japanese-specific study found that all rater groups preferred retrieved corpus sentences over zero-shot generated ones ([ACL SRW 2024](https://preview.aclanthology.org/setup/2024.acl-srw.55)). The project's own subtitle lines and Jiten's example sentences are the obvious corpus. Updating Lapis means importing a new APKG, which resets custom CSS ([Lapis releases](https://github.com/donkuri/lapis/releases)), so template edits are kept in a file that can be re-applied.

---

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space