Building AI-powered flashcards

We already built an AI study assistant that generates quizzes from any document. Quizzes are great for testing comprehension: they tell you what you know right now.

But we also need to support long-term retention through repeated practice. Flashcards are great for this!

So let’s extend the system to generate AI-powered flashcards!

Why flashcards need different AI prompting #

Here’s what I learned building both systems: the difference between quiz and flashcard generation isn’t just the output format - it’s the entire prompting strategy.

Quizzes work best with multiple-choice questions that test understanding in context. You want plausible wrong answers and explanations that connect concepts. The AI needs to think about relationships and nuanced understanding.

Flashcards are the opposite. You want simple, focused question-answer pairs that test one concept at a time. The answer should be complete but concise - no external context required. Instead of testing nuanced understanding, you’re building memory through repetition.

This means completely different prompting. For flashcards, you want clear, focused questions that isolate single concepts. The answers need to be self-contained, with no external context required. You also want varied card types (definitions, examples, processes) with a natural difficulty progression from basic to complex.

The technical approach #

Building on what already works #

The smart move was reusing the existing quiz generation infrastructure. Same caching system, same error handling, same structured LLM responses with Pydantic validation. But the prompting logic needed to be completely different.

class Flashcard(BaseModel):
    front: str = Field(..., description="Question or concept on front of card")
    back: str = Field(..., description="Answer or explanation on back of card")
    category: Optional[str] = Field(None, description="Topic category for organization")
    difficulty: Optional[str] = Field(None, description="Estimated difficulty level")

class FlashcardSet(BaseModel):
    flashcard_set_id: str = Field(..., description="Unique identifier for the flashcard set")
    flashcards: List[Flashcard] = Field(..., description="List of flashcards")
    source_material_length: int = Field(..., description="Length of source material in characters")
    total_cards: int = Field(..., description="Total number of flashcards generated")

Here’s where it gets interesting. The FlashcardGenerator follows the same architectural pattern but with specialized prompts optimized for active recall:

system_prompt = """
You are an educational flashcard generator that creates effective study cards for active recall learning.

Guidelines:
- Create clear, concise question-answer pairs from the provided text
- Focus on key concepts, definitions, and important relationships
- Front of card should be a specific question or term
- Back of card should provide complete but concise explanation
- Avoid yes/no questions - prefer "what", "how", "why", "define" questions
- Include examples when helpful for understanding
- Ensure answers are self-contained and don't require external context
"""

user_prompt = f"""
Please generate {num_cards} flashcards based on the following text:

{document_content}

Create varied flashcard types: definition cards (term to definition), concept explanation cards (concept to explanation), example cards (scenario to principle), process cards (process to steps), and relationship cards (connection to explanation).
"""

The study interface #

One of the most satisfying parts was building the flashcard interface. It needed to feel natural, like physical flashcards but better.

The core interaction is simple: click to flip, rate difficulty, move to the next card. But the details matter. Smooth CSS animations for the flip effect. Full keyboard support (arrow keys, spacebar, number keys for ratings). A progress bar that actually makes you feel like you’re getting somewhere.

Most importantly, no complexity. You pick how many cards you want (5-50), then you study. No accounts, no complicated settings, no distractions. Just you and the material.

Session state and progress tracking #

Here’s where flashcards differ from quizzes in an interesting way: they need persistent state. Quizzes are fire-and-forget: you take them once and get a score. Flashcards are meant for repeated practice over time.

The session management tracks everything you’d expect: current position, completed cards, difficulty ratings, shuffle state. But the real value is building the foundation for spaced repetition. Cards you mark as “Hard” should appear more frequently. “Easy” cards get longer intervals.

We’re not implementing full spaced repetition algorithms yet, but the session state makes it trivial to add later.

class FlashcardSessionState(BaseModel):
    session_id: str = Field(..., description="Unique session identifier")
    flashcard_set_id: str = Field(..., description="ID of the flashcard set")
    current_index: int = Field(default=0, description="Current flashcard index")
    completed_cards: List[int] = Field(default_factory=list, description="Indices of completed cards")
    difficulty_ratings: Dict[int, str] = Field(default_factory=dict, description="User difficulty ratings for cards")
    is_shuffled: bool = Field(default=False, description="Whether cards are shuffled")

What makes this approach special #

The combination of AI generation and focused interaction design creates something genuinely useful.

First, it’s personalized to your actual study material. Upload a biology textbook chapter, get biology flashcards. Switch to a computer science paper, get technical flashcards appropriate to that content. The AI understands context and generates relevant questions.

It also removes friction from the study process. Creating good flashcards manually takes hours. With AI, you generate a full deck in seconds. This makes the difference between actually using flashcards and just intending to use them.

Most importantly, the AI handles the tedious work of identifying key concepts and crafting good questions. You focus on the part that actually matters: learning the material.

Demo #

Building blocks for the future #

This foundation makes several advanced features natural extensions rather than major rewrites. We could add spaced repetition algorithms that schedule cards based on your difficulty ratings and past performance (the session state is already tracking everything needed).

Export functionality to integrate with existing tools like Anki or Quizlet would be straightforward given the structured data format.

Study analytics could track learning patterns and optimize study sessions, showing which concepts you’re struggling with and how long you spend on different topics. Collaborative features for sharing flashcard sets between users would work well since the modular architecture already isolates user sessions properly.