Make Flashcards from What You Read: Active Recall Prompts

What Makes Good Flashcards

Most flashcards fail because they test recognition instead of recall. You see the question, something feels familiar, you flip the card and say “yeah, I knew that.” But you didn’t — you recognized it. Recognizing is not remembering.

Good flashcards force active recall: you must produce the answer from memory, not just recognize it when you see it. This retrieval effort is what actually builds lasting memory. It feels harder because it is harder — and that’s the point.

The Retrieval Practice Generator (PR032) creates questions at four cognitive levels, not just one. Surface questions test basic facts. Comprehension questions test whether you understand what it means. Application questions test whether you can use the concept in a new situation. Connection questions test whether you can link it to other knowledge.

This multi-level approach prevents a common trap: you can answer surface questions perfectly while having no real understanding. By mixing question types, you discover gaps you didn’t know you had.

The Flashcard Prompt

PR032 asks AI to generate 7 questions at four levels — crucially, without providing answers. This matters. The learning happens when you attempt to answer before checking.

Here’s the workflow: paste a passage, get questions, try to answer each one out loud or in writing, then ask for answers and compare. Questions you got wrong or struggled with become your actual flashcards. Questions you answered easily? You don’t need flashcards for those — you already know them.

This approach is more efficient than flashcarding everything. Most AI flashcard tools generate cards for every fact in a passage. You end up with 50 cards, 40 of which test things you already know. The retrieval practice approach identifies what you actually need to learn.

Question Types Explained

Surface questions test basic facts and definitions. “What is the term for…?” or “According to the passage, what percentage…?” These are the easiest to answer and the least valuable for deep learning — but they verify you absorbed the raw information.

Comprehension questions test whether you understand the meaning. “Why does this phenomenon occur?” or “What is the relationship between X and Y?” These require you to explain, not just recall. If you can’t answer in your own words, you don’t really understand.

Application questions test transfer to new situations. “How would this principle apply to [different context]?” or “If the conditions changed in this way, what would happen?” These are hard — and that’s why they’re valuable. They reveal whether you can use the concept, not just describe it.

Connection questions test integration with existing knowledge. “How does this relate to [something you already know]?” or “What does this remind you of from [other field]?” These build your knowledge network, linking new ideas to established ones.

For a deeper review system using these question types over time, see Spaced Recall from Articles (C025).

Export Tips: Getting Cards into Your System

Once you’ve identified questions worth keeping, you need to get them into a spaced repetition system. Here’s how to format for the major apps:

For Anki: Ask AI to format as “Question [tab] Answer” with each card on a new line. Import using File → Import, set field separator to Tab. Or use the semicolon format: “Question;Answer” and set separator to semicolon.

For Quizlet: Ask AI to format as “Question – Answer” with each card on a new line. Use Quizlet’s import feature, set the term-definition separator to ” – ” (space-dash-space).

For Notion/Obsidian: Ask AI to format cards as toggle blocks (Notion) or callouts (Obsidian) with question visible and answer hidden. This works for quick review within your existing note system.

For cards that need more context than simple Q&A, use Cornell Notes (C021) instead — the cue column serves as built-in self-testing without needing a separate app.

Explore more memory systems in the Notes & Memory pillar or start with the complete AI for Reading hub.