You do not need another study app, you need a pipeline: notes in, well-formed flashcards out, spaced repetition doing the remembering. Here is the Anki plus local LLM workflow and the card design rules that keep it useful after three months.

📜 Table of Contents

• Division of labor
• Three card rules
• The prompt
• Importing into Anki
• Cloze cards
• Review pacing
• By subject
• FAQ

AI Study Tools: Anki Plus an LLM Workflow

The bottleneck in learning is not difficulty but recurrence: notes sink, and a single read leaves a blur after three days. Spaced repetition fixes that. What stops people is the cost of making cards — a textbook means a full day of writing. This pipeline moves the labor to a model and keeps the judgment with you.

Division of labor

Models are good at splitting paragraphs into question-answer pairs and rephrasing. They are bad at deciding what deserves memorizing, staying factually correct, and controlling granularity. So: model drafts, human filters, Anki schedules.

Three card rules

1. One point per card. A card asking for a process, its purpose and its attacks at once makes errors untraceable.
2. Answerable in one sentence. If the answer runs past two lines, split it.
3. Ask in your own words. Copying textbook headings teaches the visual position, not the concept.

The prompt

Constrain the format hard: one point per card, answers under two lines, CSV with quoted fields, 15–25 cards, and — critically — no facts beyond the supplied notes. Without that last clause the model fills gaps from training data and you cannot tell which cards are yours.

Importing into Anki

Use tab or semicolon as delimiter; commas inside text break the columns. Then pick the right deck rather than dumping everything into Default, and enable HTML if you need code or formulas.

with open("cards.txt", "w", newline="", encoding="utf-8") as f:
    csv.writer(f, delimiter="\t").writerows(rows)

Cloze cards

{{c1::...}} for definitions and formulas. Multiple cloze numbers on one card are fine, but keep them on the same topic or the review rhythm fragments.

Review pacing

Use the FSRS scheduler where available; it models your actual recall instead of a fixed interval.

By subject

Vocabulary needs bidirectional cards plus cloze examples. Medicine and law suit this pipeline best. History wants causal chains, not isolated dates. Programming needs "what does this print", not API recitation. Math needs worked steps, not pure recall.

FAQ

• Generation saves authoring time only; review time is irreducible.
• Always check numbers, dates and names by hand — bad cards are expensive to unlearn.
• Split PDFs by chapter; whole-book generation degrades attention and makes review infeasible.

Anki's import dialog and scheduler options change across versions; check official docs.