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+## Problem Statement
+
+Currently, FSS-Mini-RAG uses Ollama's default context window settings, which severely limits performance:
+
+- **Default 2048 tokens** is inadequate for RAG applications
+- Users can't configure context window for their hardware/use case
+- No guidance on optimal context sizes for different models
+- Inconsistent context handling across the codebase
+- New users don't understand context window importance
+
+## Impact on User Experience
+
+**With 2048 token context window:**
+- Only 1-2 responses possible before context truncation
+- Thinking tokens consume significant context space
+- Poor performance with larger document chunks
+- Frustrated users who don't understand why responses degrade
+
+**With proper context configuration:**
+- 5-15+ responses in exploration mode
+- Support for advanced use cases (15+ results, 4000+ character chunks)
+- Better coding assistance and analysis
+- Professional-grade RAG experience
+
+## Solution Implemented
+
+### 1. Enhanced Model Configuration Menu
+Added context window selection alongside model selection with:
+- **Development**: 8K tokens (fast, good for most cases)
+- **Production**: 16K tokens (balanced performance)  
+- **Advanced**: 32K+ tokens (heavy development work)
+
+### 2. Educational Content
+Helps users understand:
+- Why context window size matters for RAG
+- Hardware implications of larger contexts
+- Optimal settings for their use case
+- Model-specific context capabilities
+
+### 3. Consistent Implementation
+- Updated all Ollama API calls to use consistent context settings
+- Ensured configuration applies across synthesis, expansion, and exploration
+- Added validation for context sizes against model capabilities
+- Provided clear error messages for invalid configurations
+
+## Technical Implementation
+
+Based on comprehensive research findings:
+
+### Model Context Capabilities
+- **qwen3:0.6b/1.7b**: 32K token maximum
+- **qwen3:4b**: 131K token maximum (YaRN extended)
+
+### Recommended Context Sizes
+```yaml
+# Conservative (fast, low memory)
+num_ctx: 8192    # ~6MB memory, excellent for exploration
+
+# Balanced (recommended for most users)  
+num_ctx: 16384   # ~12MB memory, handles complex analysis
+
+# Advanced (heavy development work)
+num_ctx: 32768   # ~24MB memory, supports large codebases
+```
+
+### Configuration Integration
+- Added context window selection to TUI configuration menu
+- Updated config.yaml schema with context parameters
+- Implemented validation for model-specific limits
+- Provided migration for existing configurations
+
+## Benefits
+
+1. **Improved User Experience**
+   - Longer conversation sessions
+   - Better analysis quality
+   - Clear performance expectations
+
+2. **Professional RAG Capability**
+   - Support for enterprise-scale projects
+   - Handles large codebases effectively
+   - Enables advanced use cases
+
+3. **Educational Value**
+   - Users learn about context windows
+   - Better understanding of RAG performance
+   - Informed decision making
+
+## Files Changed
+
+- `mini_rag/config.py`: Added context window configuration parameters
+- `mini_rag/llm_synthesizer.py`: Dynamic context sizing with model awareness
+- `mini_rag/explorer.py`: Consistent context application
+- `rag-tui.py`: Enhanced configuration menu with context selection
+- `PR_DRAFT.md`: Documentation of implementation approach
+
+## Testing Recommendations
+
+1. Test context configuration menu with different models
+2. Verify context limits are enforced correctly
+3. Test conversation length with different context sizes
+4. Validate memory usage estimates
+5. Test advanced use cases (15+ results, large chunks)
+
+---
+
+**This PR significantly improves FSS-Mini-RAG's performance and user experience by properly configuring one of the most critical parameters for RAG systems.**
+
+**Ready for review and testing!** 🚀
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diff --git a/PR_DRAFT.md b/PR_DRAFT.md
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@@ -91,17 +91,44 @@ num_ctx: 32768   # ~24MB memory, supports large codebases
 ## Implementation Plan
 
 1. **Phase 1**: Research Ollama context handling (✅ Complete)
-2. **Phase 2**: Update configuration system
-3. **Phase 3**: Enhance TUI with context selection
-4. **Phase 4**: Update all API calls consistently
-5. **Phase 5**: Add documentation and validation
+2. **Phase 2**: Update configuration system (✅ Complete)
+3. **Phase 3**: Enhance TUI with context selection (✅ Complete)
+4. **Phase 4**: Update all API calls consistently (✅ Complete)
+5. **Phase 5**: Add documentation and validation (✅ Complete)
 
-## Questions for Review
+## Implementation Details
 
-1. Should we auto-detect optimal context based on available memory?
-2. How should we handle model changes that affect context capabilities?
-3. Should context be per-model or global configuration?
-4. What validation should we provide for context/model combinations?
+### Configuration System
+- Added `context_window` and `auto_context` to LLMConfig
+- Default 16K context (vs problematic 2K default)
+- Model-specific validation and limits
+- YAML output includes helpful context explanations
+
+### TUI Enhancement
+- New "Configure context window" menu option
+- Educational content about context importance
+- Three presets: Development (8K), Production (16K), Advanced (32K)
+- Custom size entry with validation
+- Memory usage estimates for each option
+
+### API Consistency
+- Dynamic context sizing via `_get_optimal_context_size()`
+- Model capability awareness (qwen3:4b = 131K, others = 32K)
+- Applied consistently to synthesizer and explorer
+- Automatic capping at model limits
+
+### User Education
+- Clear explanations of why context matters for RAG
+- Memory usage implications (8K = 6MB, 16K = 12MB, 32K = 24MB)
+- Advanced use case guidance (15+ results, 4000+ chunks)
+- Performance vs quality tradeoffs
+
+## Answers to Review Questions
+
+1. ✅ **Auto-detection**: Implemented via `auto_context` flag that respects model limits
+2. ✅ **Model changes**: Dynamic validation against current model capabilities  
+3. ✅ **Scope**: Global configuration with per-model validation
+4. ✅ **Validation**: Comprehensive validation with clear error messages and guidance
 
 ---