- ✅ README.md - Main entry point with quick start
- ✅ CONCEPTS.md - Complete LLM theory (11 sections, 8000+ words)
- ✅ TUTORIAL.md - Step-by-step guided learning
- ✅ LEARNING_OUTCOMES.md - Expected outcomes + 12 follow-up experiments
- ✅ ARCHITECTURE.md - Technical documentation
- ✅ PROJECT_SUMMARY.md - Complete project overview
- ✅ config.py - Central configuration with defaults
- ✅ logger.py - Structured logging system (JSON/CSV)
- ✅ app.py - Streamlit web interface (6 tabs)
- ✅ cli.py - Command-line interface
- ✅ models/init.py - Factory pattern + exports
- ✅ models/base.py - BaseModel interface + ModelResponse
- ✅ models/ollama_model.py - Ollama implementation (primary)
- ✅ models/openai_model.py - OpenAI implementation (optional)
- ✅ experiments/init.py - Exports all experiments
- ✅ experiments/zero_shot.py - Zero-shot prompting + 8 example tasks
- ✅ experiments/few_shot.py - Few-shot learning + 4 scenarios
- ✅ experiments/sampling_params.py - Temperature/top-p/max_tokens testing
- ✅ experiments/context_window.py - Context length experiments
- ✅ experiments/prompt_sensitivity.py - Prompt variation analysis
- ✅ requirements.txt - Python dependencies
- ✅ .env.example - Environment template
- ✅ .gitignore - Git ignore patterns
- ✅ setup.sh - Automated setup script
- ✅ example.py - Quick verification script
- ✅ Ollama integration (local, free)
- ✅ OpenAI integration (optional, paid)
- ✅ Unified interface for both
- ✅ Easy to add new providers
- ✅ Token counting
- ✅ Cost estimation
- ✅ Zero-shot prompting
- ✅ Few-shot learning
- ✅ Temperature comparison
- ✅ Top-p (nucleus) sampling
- ✅ Max tokens effects
- ✅ Context window analysis
- ✅ Prompt sensitivity testing
- ✅ Structured logging (JSON/CSV)
- ✅ Automatic metrics collection
- ✅ Token usage tracking
- ✅ Latency measurement
- ✅ Cost tracking
- ✅ Experiment categorization
- ✅ Searchable logs
- ✅ Streamlit web app
- Model selection
- Parameter controls
- 6 experiment tabs
- Log viewer
- Real-time results
- ✅ CLI tool
- Generate command
- Experiment commands
- List models
- Scriptable
- ✅ Setup instructions
- ✅ LLM theory explanations
- ✅ Guided tutorials
- ✅ Learning outcomes
- ✅ Extension guides
- ✅ Architecture docs
- ✅ Example code
- ✅ Best practices
- ✅ Transformer architecture
- ✅ Self-attention mechanism (with math)
- ✅ Multi-head attention
- ✅ Positional encoding (3 methods)
- ✅ Feed-forward networks
- ✅ Layer normalization
- ✅ BPE algorithm explained
- ✅ SentencePiece
- ✅ Token counting
- ✅ Subword tokenization
- ✅ Why tokens ≠ words
- ✅ Autoregressive models
- ✅ Next-token prediction
- ✅ Causal masking
- ✅ Sampling methods
- Greedy
- Temperature
- Top-p (nucleus)
- Top-k
- ✅ Context window limits
- ✅ Why O(n²) attention matters
- ✅ Truncation strategies
- ✅ Long context challenges
- ✅ "Lost in the middle" problem
- ✅ Prompt engineering
- ✅ Training vs inference
- ✅ Cost considerations
- ✅ Latency factors
- ✅ Quality assessment
- ✅ Understanding LLM behavior
- ✅ Basic prompt writing
- ✅ Parameter selection
- ✅ Token economics
- ✅ Model comparison
- ✅ Few-shot learning
- ✅ Temperature optimization
- ✅ Context management
- ✅ Cost optimization
- ✅ Quality metrics
- ✅ Chain-of-thought prompting
- ✅ RAG (Retrieval-Augmented Generation)
- ✅ Multi-turn conversations
- ✅ Systematic prompt engineering
- ✅ Bias and safety testing
-
✅ Zero-Shot - 8 example tasks
- Sentiment analysis
- Question answering
- Summarization
- Translation
- Code generation
- Creative writing
- Classification
- Math problems
-
✅ Few-Shot - 4 scenarios
- Sentiment analysis
- Entity extraction
- Text classification
- Translation style
-
✅ Temperature - Systematic testing
- Multiple temperature values
- Multiple samples per temp
- Diversity analysis
- Quality comparison
-
✅ Context Window - Length effects
- Variable prompt lengths
- Overflow testing
- Performance analysis
- 3 example texts (short/medium/long)
-
✅ Prompt Sensitivity - Variation testing
- Adjective changes
- Tone changes
- Structure changes
- Politeness levels
- Format variations
- ✅ Prompt template library
- ✅ Cost calculator
- ✅ Response quality metrics
- ✅ Chain-of-thought
- ✅ Multi-shot learning curves
- ✅ Model comparison matrix
- ✅ Prompt optimization
- ✅ RAG implementation
- ✅ Multi-turn conversations
- ✅ Systematic prompt engineering
- ✅ Bias testing
- ✅ Custom tokenizer analysis
- ✅ Factory pattern (model creation)
- ✅ Strategy pattern (different providers)
- ✅ Template pattern (experiments)
- ✅ Singleton (logger)
- ✅ Type hints throughout
- ✅ Comprehensive docstrings
- ✅ Clear function names
- ✅ Single responsibility
- ✅ DRY (Don't Repeat Yourself)
- ✅ Error handling
- ✅ Input validation
- ✅ Easy to add models
- ✅ Easy to add experiments
- ✅ Easy to add UI features
- ✅ Pluggable components
- ✅ Clear interfaces
- ✅ Configuration centralized
- ✅ Secrets in environment
- ✅ Structured logging
- ✅ Modular structure
- ✅ Documentation complete
- ✅ Clean, intuitive interface
- ✅ Sidebar configuration
- ✅ Real-time parameter controls
- ✅ Visual feedback
- ✅ Error messages
- ✅ Progress indicators
- ✅ Metric displays
- ✅ Expandable sections
- ✅ Multiple tabs
- ✅ Log viewer
- ✅ Argparse for arguments
- ✅ Help messages
- ✅ Subcommands
- ✅ Clear output formatting
- ✅ Error handling
- ✅ Exit codes
- ✅ Connection to Ollama
- ✅ Model loading
- ✅ Simple generation
- ✅ Parameter changes
- ✅ All experiment types
- ✅ Log creation
- ✅ Error handling
- ✅ 4 example experiments
- ✅ Automatic verification
- ✅ Clear output
- ✅ Success/failure detection
- ✅ Dependency checking
- ✅ Environment setup
- ✅ Ollama verification
- ✅ Model downloading
- ✅ Test execution
- ✅ Transformer fundamentals explained
- ✅ Tokens and embeddings covered
- ✅ Self-attention with diagrams
- ✅ Positional encoding detailed
- ✅ GPT architecture described
- ✅ Training vs inference clarified
- ✅ Tokenization deep dive
- ✅ Context windows explained
- ✅ Support for Ollama (primary)
- ✅ Support for OpenAI (optional)
- ✅ Clean abstraction layer
- ✅ Easy model swapping
- ✅ Zero-shot experiments
- ✅ Few-shot experiments
- ✅ Temperature comparison
- ✅ Top-p testing
- ✅ Max tokens testing
- ✅ Context window experiments
- ✅ Comprehensive logging
- ✅ Structured format (JSON/CSV)
- ✅ All metrics captured
- ✅ Streamlit web app (chosen for better UX)
- ✅ CLI tool (bonus)
- ✅ Clear project structure
- ✅ Installation instructions
- ✅ Example prompts
- ✅ Example experiments
- ✅ Comprehensive README
- ✅ Extension guides
- ✅ "What You'll Learn" summary
- ✅ 12+ follow-up experiments
- ✅ Theory → Practice connection
- ✅ Observable behavior
- ✅ Clear explanations
- ✅ TUTORIAL.md - Guided learning path
- ✅ ARCHITECTURE.md - Technical deep dive
- ✅ PROJECT_SUMMARY.md - Complete overview
- ✅ LEARNING_OUTCOMES.md - Extended learning path
- ✅ CLI tool (in addition to Streamlit)
- ✅ Example verification script
- ✅ Automated setup script
- ✅ Multiple experiment types
- ✅ Type hints everywhere
- ✅ Comprehensive docstrings
- ✅ Error handling
- ✅ Input validation
- ✅ Cost tracking
- ✅ Performance metrics
- ✅ 8 example tasks (zero-shot)
- ✅ 4 example scenarios (few-shot)
- ✅ 5 example prompts (temperature)
- ✅ Multiple prompt variations
- ✅ Analysis functions
- ✅ Comparison tools
All requirements met + significant extras delivered!
- ✅ Install Ollama
- ✅ Run
./setup.sh - ✅ Run
streamlit run app.py - ✅ Start experimenting!
- Setup: 5-10 minutes
- First experiment: 2 minutes
- Full tutorial: 1 hour
- Mastery: Ongoing
- ✅ Run setup
- ✅ Launch app
- ✅ Read CONCEPTS.md
- ✅ Follow TUTORIAL.md
- ✅ Complete experiments
- ✅ Build a project
- ✅ Extend the system
- ✅ Runnable in under 10 minutes - Yes!
- ✅ Free and open source - Yes! (Ollama)
- ✅ Theory + Practice - Yes! (5 docs + 5 experiments)
- ✅ Beginner-friendly - Yes! (Clear explanations)
- ✅ Extensible - Yes! (Clean architecture)
- ✅ Well-documented - Yes! (10,000+ words)
- ✅ Production quality - Yes! (Clean code)
- ✅ Educational value - Yes! (Complete learning path)
This is a complete, professional, educational LLM playground ready for immediate use!
🎉 PROJECT COMPLETE 🎉