Content Recommendations.md - himent12/FlashGenie GitHub Wiki

๐Ÿ”ฎ Content Recommendations

FlashGenie's Content Recommendations use AI to suggest new flashcards, identify knowledge gaps, and optimize your learning content.

๐ŸŽฏ What are Content Recommendations?

Content Recommendations are AI-powered suggestions that:

  • Generate new flashcards based on your existing content
  • Identify knowledge gaps in your current understanding
  • Suggest related topics to expand your knowledge
  • Recommend content improvements for existing cards
  • Create balanced learning paths across topics

๐Ÿ’ก Key Features

AI-Generated Flashcards

# Generate new flashcard suggestions
python -m flashgenie suggest "Spanish Vocabulary" --cards 5

Sample output:

Suggested Flashcards:
1. Q: ยฟCuรกl es la diferencia entre "ser" y "estar"?
   A: "Ser" describes permanent qualities, while "estar" describes temporary states.

2. Q: ยฟCรณmo se forma el subjuntivo presente?
   A: Form the subjunctive by taking the yo form of present indicative, drop the -o, and add the opposite vowel endings.
...

Knowledge Gap Detection

# Identify knowledge gaps
python -m flashgenie suggest "Biology" --gaps

Sample output:

Knowledge Gaps Detected:
1. Cell Respiration (connects Mitochondria and Energy concepts)
2. DNA Transcription (prerequisite for RNA Translation)
3. Enzyme Kinetics (builds on Protein Structure)

Topic Expansion

# Get related topic suggestions
python -m flashgenie suggest "Machine Learning" --topics

Sample output:

Related Topics:
1. Neural Networks (High relevance)
2. Gradient Descent (Medium relevance)
3. Overfitting & Regularization (Medium relevance)
4. Feature Engineering (Low relevance)

๐Ÿš€ Using Content Recommendations

Basic Usage

# Get basic card suggestions
python -m flashgenie suggest "History"

Advanced Usage

# Get comprehensive recommendations
python -m flashgenie suggest "Physics" \
  --cards 10 \
  --gaps \
  --topics \
  --difficulty medium \
  --format detailed

Available Parameters

Parameter Description Example
--cards Number of cards to suggest --cards 5
--gaps Identify knowledge gaps --gaps
--topics Suggest related topics --topics
--difficulty Target difficulty level --difficulty medium
--format Output format --format detailed

๐Ÿง  Recommendation Types

Card Content Suggestions

# Get specific card type suggestions
python -m flashgenie suggest "Math" --card-type definition

Available card types:

  • definition - Term explanations
  • concept - Deeper understanding
  • application - Practical usage
  • comparison - Contrasting ideas
  • process - Step-by-step procedures

Content Improvement Suggestions

# Get improvement suggestions for existing cards
python -m flashgenie suggest-improvements "Spanish Vocabulary"

Improvement categories:

  • Clarity - Clearer wording
  • Specificity - More precise information
  • Memorability - Easier to remember
  • Connections - Related concepts
  • Examples - Illustrative examples

Learning Path Recommendations

# Get a recommended learning sequence
python -m flashgenie suggest-path "Programming"

Sample output:

Recommended Learning Path:
1. Basic Syntax (Mastered)
2. Control Structures (In Progress)
3. Functions (Suggested Next)
4. Data Structures (Future)
5. Object-Oriented Programming (Future)

๐Ÿงช The Science Behind It

Content Recommendations are powered by:

  • Natural Language Processing - Understanding content semantics
  • Knowledge Graph Analysis - Mapping concept relationships
  • Spaced Repetition Optimization - Timing for new content
  • Difficulty Calibration - Appropriate challenge levels
  • Learning Path Optimization - Efficient knowledge acquisition

๐Ÿ“Š Managing Recommendations

# Accept and add suggested cards
python -m flashgenie accept-suggestions 1,3,5

# Save suggestions for later
python -m flashgenie save-suggestions --output suggestions.json

# Rate suggestion quality
python -m flashgenie rate-suggestions --quality high

๐ŸŽฏ Tips for Effective Recommendations

  1. Start with a focused deck - More specific decks yield better suggestions
  2. Add detailed tags - Tags improve recommendation relevance
  3. Rate suggestions - Feedback improves future recommendations
  4. Balance acceptance - Don't accept all suggestions automatically
  5. Combine with knowledge graphs - Visualize how suggestions fill gaps

๐Ÿš€ Next Steps


FlashGenie v1.5.0 | Last updated: June 2025