docs/TODO.md

Crossword Puzzle Webapp - TODO List

🎯 Current Status: Phases 1-5 Complete - Phase 6 (LLM Enhancement) In Progress

✅ COMPLETED PHASES (Phases 1-5)

✅ Phase 1: Project Setup

• ✅ Set up project structure and initialize repositories
  • Create frontend/ and backend/ directories
  • Initialize package.json files
  • Set up Git repository
  • Modern ES modules configuration

✅ Phase 2: Data Management (JSON-based)

• ✅ Create data structure and seed data
  • JSON-based word lists by topic (animals.json, science.json, etc.)
  • 400+ words with quality clues
  • In-memory caching system
  • WordService for data management

✅ Phase 3: Core Algorithm Development

• ✅ Implement advanced crossword generation algorithm
  • Smart word selection with crossword suitability scoring
  • Sophisticated grid placement with intersection optimization
  • Robust backtracking with timeout handling
  • Grid optimization with automatic trimming

✅ Phase 4: Backend API

• ✅ Build comprehensive backend API endpoints
  • GET /api/topics - List available topics
  • POST /api/generate - Generate crossword puzzle
  • POST /api/validate - Validate user answers
  • GET /api/words/:topic - Get words for topic
  • GET /api/health - Health check
  • Error handling, rate limiting, CORS, security headers

✅ Phase 5: Frontend Components & Features

• ✅ Create all frontend components
  • TopicSelector with multi-select buttons and visual feedback
  • PuzzleGrid with CSS Grid layout and interactive cells
  • ClueList component (Across/Down) with click navigation
  • LoadingSpinner with generation progress messages
  • PuzzleControls (Reset/Reveal/Generate buttons)

✅ Phase 6: Interactive Features

• ✅ Implement full interactive crossword functionality
  • Click-to-focus input cells with keyboard navigation
  • Real-time input validation and auto-uppercase
  • Completion detection and success messages
  • Solution reveal functionality

✅ Phase 7: User Experience & Styling

• ✅ Professional UI/UX with responsive design
  • Mobile-responsive design (tested on iPhone 14)
  • Professional color scheme and typography
  • Loading states and error messages
  • Smooth user interactions

✅ Phase 8: Deployment

• ✅ Deploy to production
  • Docker containerization
  • HuggingFace Spaces deployment
  • Public access at https://vimalk78-abc123.hf.space/
  • Production environment configuration

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🚀 CURRENT FOCUS: Phase 6 - LLM-Enhanced Dynamic Word Generation

🔄 IN PROGRESS

6.1 Core LLM Infrastructure

• Install and configure HuggingFace dependencies

  • Add @huggingface/inference package to backend
  • Set up environment variables for HF API keys
  • Create HuggingFace account and configure access tokens

• Implement EmbeddingWordService class

  • Create backend/src/services/embeddingWordService.js
  • Integrate sentence-transformers/all-MiniLM-L6-v2 model
  • Implement semantic similarity search for word generation
  • Add error handling and fallback to static words

6.2 Dynamic Word Generation

• Build topic-aware word generation system

  • Implement semantic word discovery using embeddings
  • Create word quality filtering for crossword suitability
  • Add word difficulty scoring and classification
  • Implement content validation (filter inappropriate words)

• Enhance word selection algorithm

  • Integrate AI-generated words with existing crossword algorithm
  • Optimize word selection for better grid placement
  • Add intersection-aware word generation

6.3 LLM-Powered Clue Generation

• Implement intelligent clue generation

  • Set up small language model for clue creation
  • Create template-based clue generation with topic context
  • Ensure crossword-appropriate clue formatting
  • Add clue quality validation and scoring

• Enhance clue system

  • Context-aware clue generation based on topic
  • Difficulty-matched clue complexity
  • Multiple clue variations per word
  • A/B testing for clue quality

6.4 Advanced Caching Implementation

• Design multi-tier cache architecture

  • L1: In-Memory (session cache) - existing ✅
  • L2: Redis integration for cross-session caching
  • L3: Enhanced fallback to JSON files

• Implement smart cache policies

  • Hybrid TTL + LRU eviction strategy
  • Usage-based scoring: (frequency × 0.4) + (recency × 0.3) + (cost × 0.3)
  • Adaptive TTL based on API response times and error rates
  • Topic-aware cache eviction with different TTL for popular vs niche topics

6.5 Performance & Reliability

• Build robust fallback systems

  • Graceful degradation when LLM APIs fail
  • Offline mode with cached content
  • Error recovery and retry logic with exponential backoff
  • Health monitoring for LLM services

• Optimize for performance

  • Batch word generation requests
  • Precompute popular topic combinations
  • Async generation with real-time progress indicators
  • Request deduplication and coalescence

6.6 Quality Control & Monitoring

• Implement content validation systems

  • Word appropriateness filtering
  • Crossword intersection analysis for AI-generated words
  • Difficulty consistency checking
  • User feedback collection system

• Add continuous improvement features

  • Analytics for AI-generated content quality metrics
  • Model performance monitoring
  • User rating system for generated puzzles
  • A/B testing framework for different models

🎯 FUTURE ENHANCEMENTS (Phase 7+)

7.1 Advanced Features

• Custom topic support

  • User-defined topic combinations
  • Real-time topic similarity recommendations
  • Trending topic suggestions
  • Personal topic history

• Enhanced difficulty system

  • AI-driven difficulty assessment
  • Personalized difficulty scaling
  • Learning curve adaptation
  • Challenge progression system

7.2 User Experience Improvements

• Advanced UI features
  • Puzzle saving and sharing
  • User accounts and puzzle history
  • Social features (puzzle sharing, challenges)
  • Mobile app development

7.3 Analytics & Business Intelligence

• User behavior analytics
• Content performance metrics
• API usage monitoring
• Cost optimization analysis

📋 CURRENT SPRINT TASKS

Week 1-2 (Current):

1. Set up HuggingFace integration
2. Create EmbeddingWordService class
3. Implement basic dynamic word generation
4. Add Redis caching layer

Week 3-4:

1. LLM clue generation system
2. Quality control and validation
3. Performance optimization
4. Comprehensive testing

Week 5-6:

1. Advanced caching strategies
2. Error handling and fallbacks
3. User feedback collection
4. Production deployment of Phase 6

🔗 Key Files Created ✅

• ✅ backend/src/services/crosswordGenerator.js - Advanced algorithm
• ✅ backend/src/controllers/puzzleController.js - API handlers
• ✅ backend/src/services/wordService.js - Data management
• ✅ frontend/src/hooks/useCrossword.js - State management
• ✅ frontend/src/utils/gridHelpers.js - Grid utilities
• ✅ frontend/src/styles/puzzle.css - Professional styling
• ✅ backend/src/routes/api.js - API routing
• ✅ Dockerfile - Production deployment

🎯 Next Priority Tasks

1. LLM Integration Setup - HuggingFace API and embeddings
2. Dynamic Word Generation - Semantic similarity-based word discovery
3. Advanced Caching - Redis integration with smart eviction policies
4. Quality Control - Validation and feedback systems