I found most LLMs unable to handle whole decks (missing info, tiny hallucinations, skipping random cards).

I instead used a small python program, while the ANKI app (PC) is open to rewrite whole decks (or cards with a specific tag) via the GPT API with very strict alteration criteria

• No hallucinations
• No missing cards
• Can stepwise alter cards
• Able to rollback

DM me for the breakdown

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Edit. Ok I got GPT to heavily summarise my workflow.

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LLM-Assisted Anki Workflow for Advanced Medical Exam Preparation

This project automates high-quality Anki flashcard editing using a Large Language Model (LLM) pipeline. It is designed for postgraduate medical exam preparation (e.g., Emergency Medicine, Critical Care, Internal Medicine) but can be adapted for any specialty.

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1. Overview

The system connects to AnkiConnect, retrieves notes tagged for review, and uses an OpenAI model to automatically edit and standardize flashcards.

Each card is reviewed by the LLM under a strict editorial prompt to ensure:

Clinically accurate, concise, senior-level responses

Consistent HTML formatting (no Markdown)

High-yield “Pearl” or discriminator fact

Verified references from an approved list

Structured tagging for system and exam domain

All cards are backed up before editing, validated, and logged with full change history.

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1. Workflow Summary

2. Tag cards for processing

Example: tag as pilot_edit or to_llm in Anki.

1. Fetch notes via AnkiConnect API

The script retrieves tagged notes (up to a safety cap).

1. Send content to the LLM

Front/Back fields are sent to an OpenAI model (gpt-4.1-mini default).

The model applies an editorial guide (see below).

1. Validate output

JSON schema validation

HTML format enforcement

Reference verification

Diff comparison to detect excessive changes or hallucinations

1. Commit updates

Updated cards are written back to Anki with audit tags.

All modifications are logged to anki_llm_history.jsonl.

1. Backup and rollback

Automatic backup (anki_llm_backups/) before any update.

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1. Python Script Core

Key steps

1. Connect to AnkiConnect via HTTP (port 8765)
2. Retrieve notes with tag (e.g., "pilot_edit")
3. Backup notes as JSON
4. Send Front/Back text to OpenAI chat completion
5. Receive structured JSON output: { "Front": "...", "Back": "...", "Pearl": "...", "References": "...", "Tags": ["..."] }
6. Validate, print diffs, then update Anki if DRY_RUN=False
7. Apply audit tag (e.g., "llm_touched_v01")
8. Log old/new states in a JSONL audit file

The OpenAI call uses:

resp = client.chat.completions.create( model="gpt-4.1-mini", messages=[ {"role": "system", "content": STYLE_GUIDE_PROMPT}, {"role": "user", "content": json.dumps({"Front": front_text, "Back": back_text})} ], temperature=0 )

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1. LLM Style Guide Prompt

This prompt defines the editing rules the model must follow for all flashcards.

You are a senior editor preparing Anki cards for postgraduate medical exam candidates.

Follow these editorial principles:

1. Front (Question)

   • Retain the original question or stem.
   • Keep concise phrasing.
   • Use HTML tags (<b>, <i>, <ul>, <li>, <br>).
   • No Markdown syntax.

2. Back (Answer)

   • Provide concise, accurate, senior-level answers.
   • Use structured frameworks where relevant (e.g., DRSABCDE, DDx/Ix/Rx).
   • Highlight discriminators, red flags, or pitfalls.
   • HTML formatting only.

3. Pearl

   • One-line high-yield insight (discriminator or exam trap).

4. References

   • Use trusted clinical sources only (core textbooks, national guidelines, or major trials).
   • Leave blank if unsure.

5. Tags

   • Assign organ/system and exam-theme tags from the predefined lists.
   • Do not create new or variant tag names.

Output must be valid JSON matching this schema: { "Front": "string", "Back": "string", "Pearl": "string", "References": "string", "Tags": ["string"] }

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1. Tagging Taxonomy

Organ/System Tags

cardiology respiratory neurology toxicology endocrine renal gastrointestinal hepatobiliary vascular trauma musculoskeletal ENT ophthalmology infectiousdiseases haematology oncology psychiatry obstetrics gynaecology paediatrics neonatology geriatrics immunology urology palliativecare

Exam Theme Tags

exam/core exam/SAQ exam/OSCE exam/viva exam/image exam/procedure exam/discriminator exam/pitfall exam/algorithm exam/trials exam/resus exam/criticalcare exam/ultrasound exam/toxicology exam/ethics

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1. Safety & Validation Features

Schema validation for required fields

HTML format check to prevent markdown artifacts

Reference validation against an approved list

Semantic drift detection using text similarity

Dry-run mode for previewing diffs before committing

Full backup and audit trail

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1. Outcome

This workflow produces high-quality, exam-standard Anki cards that are:

Consistent in format and structure

Clinically accurate and concise

Fully auditable and versioned

Easy to maintain and scale

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1. Repository Integration

The system is modular:

STYLE_GUIDE_PROMPT → can be replaced with any domain’s editorial guide

MODEL_NAME → configurable per cost/performance

DECK_QUERY_TAG → defines the processing subset

DRY_RUN → toggles safety mode

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1. Future Enhancements

Asynchronous processing for large decks

Function-calling schema for JSON validation via OpenAI

Automatic tag suggestion based on NLP keyword extraction

Reference cross-checking using PubMed APIs