docs: Add comprehensive financial extraction improvement plan
This plan addresses all 10 pending todos with detailed implementation steps: Priority 1 (Weeks 1-2): Research & Analysis - Review older commits for historical patterns - Research best practices for financial data extraction Priority 2 (Weeks 3-4): Performance Optimization - Reduce processing time from 178s to <120s - Implement tiered model approach, parallel processing, prompt optimization Priority 3 (Weeks 5-6): Testing & Validation - Add comprehensive unit tests (>80% coverage) - Test invalid value rejection, cross-period validation, period identification Priority 4 (Weeks 7-8): Monitoring & Observability - Track extraction success rates, error patterns - Implement user feedback collection Priority 5 (Weeks 9-11): Code Quality & Documentation - Optimize prompt size (20-30% reduction) - Add financial data visualization UI - Document extraction strategies Priority 6 (Weeks 12-14): Advanced Features - Compare RAG vs Simple extraction approaches - Add confidence scores for extractions Includes detailed tasks, deliverables, success criteria, timeline, and risk mitigation strategies.
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# Financial Extraction Improvement Plan
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## Overview
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This document outlines a comprehensive plan to address all pending todos related to financial extraction improvements. The plan is organized by priority and includes detailed implementation steps, success criteria, and estimated effort.
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## Current Status
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### ✅ Completed
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- Test financial extraction with Stax Holding Company CIM - All values correct
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- Implement deterministic parser fallback - Integrated into simpleDocumentProcessor
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- Implement few-shot examples - Added comprehensive examples for PRIMARY table identification
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- Fix primary table identification - Financial extraction now correctly identifies PRIMARY table
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### 📊 Current Performance
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- **Accuracy**: 100% for Stax CIM test case (FY-3: $64M, FY-2: $71M, FY-1: $71M, LTM: $76M)
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- **Processing Time**: ~178 seconds (3 minutes) for full document
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- **API Calls**: 2 (1 financial extraction + 1 main extraction)
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- **Completeness**: 96.9%
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---
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## Priority 1: Research & Analysis (Weeks 1-2)
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### Todo 1: Review Older Commits for Historical Patterns
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**Objective**: Understand how financial extraction worked in previous versions to identify what was effective.
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**Tasks**:
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1. Review commit history (2-3 hours)
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- Check commit 185c780 (Claude 3.7 implementation)
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- Check commit 5b3b1bf (Document AI fixes)
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- Check commit 0ec3d14 (multi-pass extraction)
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- Document prompt structures, validation logic, and error handling
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2. Compare prompt simplicity (2 hours)
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- Extract prompts from older commits
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- Compare verbosity, structure, and clarity
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- Identify what made older prompts effective
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- Document key differences
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3. Analyze deterministic parser usage (2 hours)
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- Review how financialTableParser.ts was used historically
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- Check integration patterns with LLM extraction
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- Identify successful validation strategies
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4. Create comparison document (1 hour)
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- Document findings in docs/financial-extraction-evolution.md
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- Include before/after comparisons
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- Highlight lessons learned
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**Deliverables**:
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- Analysis document comparing old vs new approaches
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- List of effective patterns to reintroduce
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- Recommendations for prompt simplification
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**Success Criteria**:
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- Complete analysis of 3+ historical commits
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- Documented comparison of prompt structures
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- Clear recommendations for improvements
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---
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### Todo 2: Review Best Practices for Financial Data Extraction
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**Objective**: Research industry best practices and academic approaches to improve extraction accuracy and reliability.
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**Tasks**:
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1. Academic research (4-6 hours)
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- Search for papers on LLM-based tabular data extraction
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- Review financial document parsing techniques
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- Study few-shot learning for table extraction
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2. Industry case studies (3-4 hours)
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- Research how companies extract financial data
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- Review open-source projects (Tabula, Camelot)
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- Study financial data extraction libraries
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3. Prompt engineering research (2-3 hours)
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- Study chain-of-thought prompting for tables
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- Review few-shot example selection strategies
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- Research validation techniques for structured outputs
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4. Hybrid approach research (2-3 hours)
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- Review deterministic + LLM hybrid systems
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- Study error handling patterns
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- Research confidence scoring methods
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5. Create best practices document (2 hours)
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- Document findings in docs/financial-extraction-best-practices.md
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- Include citations and references
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- Create implementation recommendations
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**Deliverables**:
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- Best practices document with citations
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- List of recommended techniques
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- Implementation roadmap
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**Success Criteria**:
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- Reviewed 10+ academic papers or industry case studies
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- Documented 5+ applicable techniques
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- Clear recommendations for implementation
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---
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## Priority 2: Performance Optimization (Weeks 3-4)
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### Todo 3: Reduce Processing Time Without Sacrificing Accuracy
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**Objective**: Reduce processing time from ~178 seconds to <120 seconds while maintaining 100% accuracy.
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**Strategies**:
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#### Strategy 3.1: Model Selection Optimization
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- Use Claude Haiku 3.5 for initial extraction (faster, cheaper)
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- Use Claude Sonnet 3.7 for validation/correction (more accurate)
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- Expected impact: 30-40% time reduction
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#### Strategy 3.2: Parallel Processing
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- Extract independent sections in parallel
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- Financial, business description, market analysis, etc.
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- Expected impact: 40-50% time reduction
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#### Strategy 3.3: Prompt Optimization
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- Remove redundant instructions
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- Use more concise examples
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- Expected impact: 10-15% time reduction
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#### Strategy 3.4: Caching Common Patterns
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- Cache deterministic parser results
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- Cache common prompt templates
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- Expected impact: 5-10% time reduction
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**Deliverables**:
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- Optimized processing pipeline
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- Performance benchmarks
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- Documentation of time savings
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**Success Criteria**:
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- Processing time reduced to <120 seconds
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- Accuracy maintained at 95%+
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- API calls optimized
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---
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## Priority 3: Testing & Validation (Weeks 5-6)
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### Todo 4: Add Unit Tests for Financial Extraction Validation Logic
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**Test Categories**:
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1. Invalid Value Rejection
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- Test rejection of values < $10M for revenue
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- Test rejection of negative EBITDA when should be positive
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- Test rejection of unrealistic growth rates
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2. Cross-Period Validation
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- Test revenue growth consistency
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- Test EBITDA margin trends
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- Test period-to-period validation
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3. Numeric Extraction
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- Test extraction of values in millions
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- Test extraction of values in thousands (with conversion)
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- Test percentage extraction
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4. Period Identification
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- Test years format (2021-2024)
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- Test FY-X format (FY-3, FY-2, FY-1, LTM)
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- Test mixed format with projections
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**Deliverables**:
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- Comprehensive test suite with 50+ test cases
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- Test coverage >80% for financial validation logic
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- CI/CD integration
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**Success Criteria**:
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- All test cases passing
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- Test coverage >80%
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- Tests catch regressions before deployment
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---
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## Priority 4: Monitoring & Observability (Weeks 7-8)
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### Todo 5: Monitor Production Financial Extraction Accuracy
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**Monitoring Components**:
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1. Extraction Success Rate Tracking
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- Track extraction success/failure rates
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- Log extraction attempts and outcomes
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- Set up alerts for issues
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2. Error Pattern Analysis
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- Categorize errors by type
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- Track error trends over time
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- Identify common error patterns
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3. User Feedback Collection
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- Add UI for users to flag incorrect extractions
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- Store feedback in database
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- Use feedback to improve prompts
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**Deliverables**:
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- Monitoring dashboard
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- Alert system
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- Error analysis reports
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- User feedback system
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**Success Criteria**:
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- Real-time monitoring of extraction accuracy
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- Alerts trigger for issues
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- User feedback collected and analyzed
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---
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## Priority 5: Code Quality & Documentation (Weeks 9-11)
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### Todo 6: Optimize Prompt Size for Financial Extraction
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**Current State**: ~28,000 tokens
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**Optimization Strategies**:
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1. Remove redundancy (target: 30% reduction)
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2. Use more concise examples (target: 40-50% reduction)
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3. Focus on critical rules only
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**Success Criteria**:
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- Prompt size reduced by 20-30%
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- Accuracy maintained at 95%+
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- Processing time improved
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---
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### Todo 7: Add Financial Data Visualization
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**Implementation**:
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1. Backend API for validation and corrections
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2. Frontend component for preview and editing
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3. Confidence score display
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4. Trend visualization
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**Success Criteria**:
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- Users can preview financial data
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- Users can correct incorrect values
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- Corrections are stored and used for improvement
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---
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### Todo 8: Document Extraction Strategies
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**Documentation Structure**:
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1. Table Format Catalog (years, FY-X, mixed formats)
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2. Extraction Patterns (primary table, period mapping)
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3. Best Practices Guide (prompt engineering, validation)
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**Deliverables**:
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- Comprehensive documentation in docs/financial-extraction-guide.md
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- Format catalog with examples
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- Pattern library
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- Best practices guide
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---
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## Priority 6: Advanced Features (Weeks 12-14)
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### Todo 9: Compare RAG vs Simple Extraction for Financial Accuracy
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**Comparison Study**:
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1. Test both approaches on 10+ CIM documents
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2. Analyze results and identify best approach
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3. Design and implement hybrid if beneficial
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**Success Criteria**:
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- Clear understanding of which approach is better
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- Hybrid approach implemented if beneficial
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- Accuracy improved or maintained
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---
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### Todo 10: Add Confidence Scores to Financial Extraction
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**Implementation**:
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1. Design scoring algorithm (parser agreement, value consistency)
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2. Implement confidence calculation
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3. Flag low-confidence extractions for review
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4. Add review interface
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**Success Criteria**:
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- Confidence scores calculated for all extractions
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- Low-confidence extractions flagged
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- Review process implemented
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---
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## Implementation Timeline
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- **Weeks 1-2**: Research & Analysis
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- **Weeks 3-4**: Performance Optimization
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- **Weeks 5-6**: Testing & Validation
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- **Weeks 7-8**: Monitoring
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- **Weeks 9-11**: Code Quality & Documentation
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- **Weeks 12-14**: Advanced Features
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## Success Metrics
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- **Accuracy**: Maintain 95%+ accuracy
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- **Performance**: <120 seconds processing time
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- **Reliability**: 99%+ extraction success rate
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- **Test Coverage**: >80% for financial validation
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- **User Satisfaction**: <5% manual correction rate
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## Next Steps
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1. Review and approve this plan
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2. Prioritize todos based on business needs
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3. Assign resources
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4. Begin Week 1 tasks
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