docs/DOCUMENTATION_INDEX.md

RAG Capstone Project - TRACE Metrics Documentation Index

📚 Complete Documentation Suite

This document provides an index of all explanation materials for understanding how GPT Labeling Prompts are used to calculate TRACE metrics.

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📄 Documentation Files

1. TRACE_METRICS_QUICK_REFERENCE.md ⭐ START HERE

• Size: 8.4 KB
• Purpose: Quick reference guide with all key formulas
• Contains:
  • Executive summary
  • Complete data flow
  • 4 TRACE metric definitions
  • Mathematical formulas
  • Practical example with calculations
  • Key insights and advantages
• Best For: Quick lookup, understanding the basics

2. TRACE_METRICS_EXPLANATION.md 📖 DETAILED GUIDE

• Size: 16.7 KB
• Purpose: Comprehensive explanation of the entire process
• Contains:
  • Step-by-step breakdown (4 main steps)
  • GPT prompt generation details
  • LLM response format specification
  • JSON parsing procedure
  • Detailed calculation for each metric
  • Complete end-to-end example
  • Data flow diagram (text-based)
  • Code references with line numbers
• Best For: Deep understanding, implementation details

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🎨 Visual Diagrams

3. TRACE_Metrics_Flow.png 📊 PROCESS FLOW

• Size: 306 KB (300 DPI, high quality)
• Purpose: Visual representation of 8-step calculation process
• Shows:
  1. Input preparation
  2. Sentencization
  3. Prompt generation
  4. LLM API call
  5. JSON response
  6. Data extraction
  7. Metric calculation (4 metrics)
  8. Final output
• Includes: Example calculation with expected values
• Best For: Presentations, quick visual reference

4. Sentence_Mapping_Example.png 🎯 SENTENCE-LEVEL MAPPING

• Size: 255 KB (300 DPI, high quality)
• Purpose: Shows how sentences are mapped to support information
• Shows:
  • Retrieved documents (with relevance marking)
  • Response sentences
  • Support mapping (which docs support which sentences)
  • Metric calculations from the mapping
  • Color-coded legend
• Best For: Understanding sentence-level evaluation

5. RAG_Architecture_Diagram.png 🏗️ SYSTEM ARCHITECTURE

• Size: 872 KB (300 DPI, highest quality)
• Purpose: Complete system architecture with Judge component
• Shows 3 main sections:
  1. Collection Creation (left): Data ingestion through 6 chunking strategies and 8 embedding models
  2. TRACE Evaluation Framework (center): The 4 core metrics with formulas
  3. Judge Evaluation (right): LLM-based evaluation pipeline
• Best For: System overview, presentations, publications

6. RAG_Data_Flow_Diagram.png 🔄 END-TO-END DATA FLOW

• Size: 491 KB (300 DPI, high quality)
• Purpose: Detailed 7-step data flow from query to results
• Shows:
  1. Query Processing
  2. Retrieval
  3. Response Generation
  4. Evaluation Setup
  5. Judge Evaluation
  6. Metric Calculation
  7. Output
• Includes: Code file references for each step
• Best For: Understanding full pipeline, training materials

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🎤 Presentation Materials

7. RAG_Capstone_Project_Presentation.pptx 📽️ FULL PRESENTATION

• Size: 57.7 KB
• Total Slides: 20
• Includes:
  • Project overview
  • RAG pipeline architecture
  • 6 chunking strategies
  • 8 embedding models
  • RAG evaluation challenge
  • TRACE framework details
  • LLM-based evaluation methodology
  • Advanced features
  • Performance results
  • Use cases and future roadmap
• Best For: Presentations to stakeholders, conference talks

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🗺️ How to Navigate This Documentation

👨‍💼 For Managers/Stakeholders:

1. Start with: RAG_Capstone_Project_Presentation.pptx
2. Visualize: RAG_Architecture_Diagram.png
3. Details: TRACE_METRICS_QUICK_REFERENCE.md

👨‍💻 For Developers:

1. Start with: TRACE_METRICS_QUICK_REFERENCE.md
2. Deep dive: TRACE_METRICS_EXPLANATION.md
3. Code references in explanation documents
4. Visualize: TRACE_Metrics_Flow.png and Sentence_Mapping_Example.png

👨‍🔬 For Researchers:

1. Read: TRACE_METRICS_EXPLANATION.md
2. Review: RAG_Data_Flow_Diagram.png
3. Study: Code files in advanced_rag_evaluator.py
4. Reference: All visual diagrams for publications

👨‍🎓 For Learning/Training:

1. Start: TRACE_METRICS_QUICK_REFERENCE.md
2. Visual: TRACE_Metrics_Flow.png
3. Example: Sentence_Mapping_Example.png
4. Deep: TRACE_METRICS_EXPLANATION.md
5. Presentation: RAG_Capstone_Project_Presentation.pptx

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🔍 Quick Reference: What Each File Explains

Document	Explains	Format
Quick Reference	What, Why, How	Markdown
Detailed Explanation	Deep technical details	Markdown
TRACE Flow	Step-by-step process	Image (PNG)
Sentence Mapping	Sentence-level details	Image (PNG)
Architecture	System design	Image (PNG)
Data Flow	Complete pipeline	Image (PNG)
Presentation	Overview + business case	Slides (PPTX)

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🎯 The Four TRACE Metrics (Quick Recap)

Metric	Measures	Formula	Range
R (Relevance)	% of docs relevant to query	|relevant| / 20	[0,1]
T (Utilization)	% of relevant docs used	|used| / |relevant|	[0,1]
C (Completeness)	% of relevant info covered	|R∩T| / |R|	[0,1]
A (Adherence)	No hallucinations (boolean)	All fully_supported?	{0,1}

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📊 Data Sources for Metrics

All metrics are calculated from the GPT Labeling Response JSON:

all_relevant_sentence_keys      → Used for R, T, C metrics
all_utilized_sentence_keys      → Used for T, C metrics
sentence_support_information[]  → Used for A metric (fully_supported flags)
overall_supported              → Metadata

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🔗 Related Code Files

The actual implementation can be found in:

• advanced_rag_evaluator.py - Main evaluation engine

  • Lines 305-350: GPT Labeling Prompt Template
  • Lines 470-552: Get & Parse GPT Response
  • Lines 554-609: Calculate TRACE Metrics

• llm_client.py - Groq API integration

  • LLM API calls
  • Rate limiting
  • Response handling

• streamlit_app.py - UI for viewing results

  • Evaluation display
  • Metric visualization
  • JSON download

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🚀 Using This Documentation

For Implementation:

1. Read TRACE_METRICS_QUICK_REFERENCE.md for understanding
2. Reference TRACE_METRICS_EXPLANATION.md for details
3. Check code in advanced_rag_evaluator.py for actual implementation
4. Use flow diagrams for debugging/verification

For Explanation:

1. Start with Quick Reference for overview
2. Use flow diagrams for visual explanation
3. Reference Detailed Explanation for specifics
4. Show Architecture/Data Flow diagrams for context

For Documentation:

1. Include all diagrams in technical documentation
2. Use Presentation slides for stakeholder communication
3. Reference Quick Reference in README files
4. Link to Detailed Explanation in code comments

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📈 Document Quality

All documents are production-ready:

• ✅ Diagrams: 300 DPI high resolution
• ✅ Markdown: Properly formatted with code examples
• ✅ Presentation: 20 professional slides
• ✅ Content: Complete with examples and explanations
• ✅ Consistency: Aligned across all materials

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🎓 Learning Path Recommendation

Beginner (2-3 hours):

1. Presentation (5 min overview)
2. Quick Reference (15 min)
3. TRACE Flow diagram (10 min)
4. Sentence Mapping example (15 min)
5. Architecture diagram (10 min)

Intermediate (1-2 days):

1. All above materials
2. Detailed Explanation (30 min)
3. Code walkthrough (1 hour)
4. Run example evaluation (30 min)

Advanced (Full understanding):

1. All materials above
2. Implement custom evaluation
3. Modify prompts and metrics
4. Contribute improvements

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📞 Questions?

Refer to:

• "What is TRACE?" → Quick Reference or Presentation
• "How is X calculated?" → Detailed Explanation
• "Show me the flow" → Flow diagrams
• "Why GPT labeling?" → Architecture/Explanation docs
• "How to implement?" → Code files + Explanation

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✨ Summary

This documentation suite provides complete understanding of the GPT Labeling → TRACE Metrics calculation process from multiple angles:

• Visual learners: Diagrams and presentation
• Detail-oriented: Markdown explanations with examples
• Implementers: Code references with line numbers
• Presenters: Professional slides and diagrams
• Researchers: Detailed methodology and formulas

All materials are cross-referenced and ready for production use.