# Forensic-Grade RAG Implementation Roadmap

## Context & Current State Analysis

You have access to a forensic tools recommendation system built with:
- **Embeddings-based retrieval** (src/utils/embeddings.ts)
- **Multi-stage AI pipeline** (src/utils/aiPipeline.ts) 
- **Micro-task processing** for detailed analysis
- **Rate limiting and queue management** (src/utils/rateLimitedQueue.ts)
- **YAML-based tool database** (src/data/tools.yaml)

**Current Architecture**: Basic RAG (Retrieve → AI Selection → Micro-task Generation)

**Target Architecture**: Forensic-Grade RAG with transparency, objectivity, and reproducibility

## Implementation Roadmap

### PHASE 1: Configuration Externalization & AI Architecture Enhancement (Weeks 1-2)

#### 1.1 Complete Configuration Externalization
**Objective**: Remove all hard-coded values from codebase (except AI prompts)

**Tasks**:
1. **Create comprehensive configuration schema** in `src/config/`
   - `forensic-scoring.yaml` - All scoring criteria, weights, thresholds
   - `ai-models.yaml` - AI model configurations and routing
   - `system-parameters.yaml` - Rate limits, queue settings, processing parameters
   - `validation-criteria.yaml` - Expert validation rules, bias detection parameters

2. **Implement configuration loader** (`src/utils/configLoader.ts`)
   - Hot-reload capability for configuration changes
   - Environment-specific overrides (dev/staging/prod)
   - Configuration validation and schema enforcement
   - Default fallbacks for missing values

3. **Audit existing codebase** for hard-coded values:
   - Search for literal numbers, strings, arrays in TypeScript files
   - Extract to configuration files with meaningful names
   - Ensure all thresholds (similarity scores, rate limits, token counts) are configurable

#### 1.2 Dual AI Model Architecture Implementation
**Objective**: Implement large + small model strategy for optimal cost/performance

**Tasks**:
1. **Extend environment configuration**:
   ```
   # Strategic Analysis Model (Large, Few Tokens)
   AI_STRATEGIC_ENDPOINT=
   AI_STRATEGIC_API_KEY=
   AI_STRATEGIC_MODEL=mistral-large-latest
   AI_STRATEGIC_MAX_TOKENS=500
   AI_STRATEGIC_CONTEXT_WINDOW=32000
   
   # Content Generation Model (Small, Many Tokens)  
   AI_CONTENT_ENDPOINT=
   AI_CONTENT_API_KEY=
   AI_CONTENT_MODEL=mistral-small-latest
   AI_CONTENT_MAX_TOKENS=2000
   AI_CONTENT_CONTEXT_WINDOW=8000
   ```

2. **Create AI router** (`src/utils/aiRouter.ts`):
   - Route different task types to appropriate models
   - **Strategic tasks** → Large model: tool selection, bias analysis, methodology decisions
   - **Content tasks** → Small model: descriptions, explanations, micro-task outputs
   - Automatic fallback logic if primary model fails
   - Usage tracking and cost optimization

3. **Update aiPipeline.ts**:
   - Replace single `callAI()` method with task-specific methods
   - Implement intelligent routing based on task complexity
   - Add token estimation for optimal model selection

### PHASE 2: Evidence-Based Scoring Framework (Weeks 3-5)

#### 2.1 Forensic Scoring Engine Implementation
**Objective**: Replace subjective AI selection with objective, measurable criteria

**Tasks**:
1. **Create scoring framework** (`src/scoring/ForensicScorer.ts`):
   ```typescript
   interface ScoringCriterion {
     name: string;
     weight: number;
     methodology: string;
     dataSources: string[];
     calculator: (tool: Tool, scenario: Scenario) => Promise<CriterionScore>;
   }
   
   interface CriterionScore {
     value: number;           // 0-100
     confidence: number;      // 0-100  
     evidence: Evidence[];
     lastUpdated: Date;
   }
   ```

2. **Implement core scoring criteria**:
   - **Court Admissibility Scorer**: Based on legal precedent database
   - **Scientific Validity Scorer**: Based on peer-reviewed research citations
   - **Methodology Alignment Scorer**: NIST SP 800-86 compliance assessment
   - **Expert Consensus Scorer**: Practitioner survey data integration
   - **Error Rate Scorer**: Known false positive/negative rates

3. **Build evidence provenance system**:
   - Track source of every score component
   - Maintain citation database for all claims
   - Version control for scoring methodologies
   - Automatic staleness detection for outdated evidence

#### 2.2 Deterministic Core Implementation  
**Objective**: Ensure reproducible results for identical inputs

**Tasks**:
1. **Implement deterministic pipeline** (`src/analysis/DeterministicAnalyzer.ts`):
   - Rule-based scenario classification (SCADA/Mobile/Network/etc.)
   - Mathematical scoring combination (weighted averages, not AI decisions)
   - Consistent tool ranking algorithms
   - Reproducibility validation tests

2. **Add AI enhancement layer**:
   - AI provides explanations, NOT decisions
   - AI generates workflow descriptions based on deterministic selections
   - AI creates contextual advice around objective tool choices

### PHASE 3: Transparency & Audit Trail System (Weeks 4-6)

#### 3.1 Complete Audit Trail Implementation
**Objective**: Track every decision with forensic-grade documentation

**Tasks**:
1. **Create audit framework** (`src/audit/AuditTrail.ts`):
   ```typescript
   interface ForensicAuditTrail {
     queryId: string;
     userQuery: string;
     processingSteps: AuditStep[];
     finalRecommendation: RecommendationWithEvidence;
     reproducibilityHash: string;
     validationStatus: ValidationStatus;
   }
   
   interface AuditStep {
     stepName: string;
     input: any;
     methodology: string;
     output: any;
     evidence: Evidence[];
     confidence: number;
     processingTime: number;
     modelUsed?: string;
   }
   ```

2. **Implement evidence citation system**:
   - Automatic citation generation for all claims
   - Link to source standards (NIST, ISO, RFC)
   - Reference scientific papers for methodology choices
   - Track expert validation contributors

3. **Build explanation generator**:
   - Human-readable reasoning for every recommendation
   - "Why this tool" and "Why not alternatives" explanations
   - Confidence level communication
   - Uncertainty quantification

#### 3.2 Bias Detection & Mitigation System
**Objective**: Actively detect and correct recommendation biases

**Tasks**:
1. **Implement bias detection** (`src/bias/BiasDetector.ts`):
   - **Popularity bias**: Over-recommendation of well-known tools
   - **Availability bias**: Preference for easily accessible tools
   - **Recency bias**: Over-weighting of newest tools
   - **Cultural bias**: Platform or methodology preferences

2. **Create mitigation strategies**:
   - Automatic bias adjustment algorithms
   - Diversity requirements for recommendations
   - Fairness metrics across tool categories
   - Bias reporting in audit trails

### PHASE 4: Expert Validation & Learning System (Weeks 6-8)

#### 4.1 Expert Review Integration
**Objective**: Enable forensic experts to validate and improve recommendations

**Tasks**:
1. **Build expert validation interface** (`src/validation/ExpertReview.ts`):
   - Structured feedback collection from forensic practitioners
   - Agreement/disagreement tracking with detailed reasoning
   - Expert consensus building over time
   - Minority opinion preservation

2. **Implement validation loop**:
   - Flag recommendations requiring expert review
   - Track expert validation rates and patterns
   - Update scoring based on real-world feedback
   - Methodology improvement based on expert input

#### 4.2 Real-World Case Learning
**Objective**: Learn from actual forensic investigations

**Tasks**:
1. **Create case study integration** (`src/learning/CaseStudyLearner.ts`):
   - Anonymous case outcome tracking
   - Tool effectiveness measurement in real scenarios
   - Methodology success/failure analysis
   - Continuous improvement based on field results

2. **Implement feedback loops**:
   - Post-case recommendation validation
   - Tool performance tracking in actual investigations
   - Methodology refinement based on outcomes
   - Success rate improvement over time

### PHASE 5: Advanced Features & Scientific Rigor (Weeks 7-10)

#### 5.1 Confidence & Uncertainty Quantification
**Objective**: Provide scientific confidence levels for all recommendations

**Tasks**:
1. **Implement uncertainty quantification** (`src/uncertainty/ConfidenceCalculator.ts`):
   - Statistical confidence intervals for scores
   - Uncertainty propagation through scoring pipeline
   - Risk assessment for recommendation reliability
   - Alternative recommendation ranking

2. **Add fallback recommendation system**:
   - Multiple ranked alternatives for each recommendation
   - Contingency planning for tool failures
   - Risk-based recommendation portfolios
   - Sensitivity analysis for critical decisions

#### 5.2 Reproducibility Testing Framework
**Objective**: Ensure consistent results across time and implementations

**Tasks**:
1. **Build reproducibility testing** (`src/testing/ReproducibilityTester.ts`):
   - Automated consistency validation
   - Inter-rater reliability testing
   - Cross-temporal stability analysis
   - Version control for methodology changes

2. **Implement quality assurance**:
   - Continuous integration for reproducibility
   - Regression testing for methodology changes
   - Performance monitoring for consistency
   - Alert system for unexpected variations

### PHASE 6: Integration & Production Readiness (Weeks 9-12)

#### 6.1 System Integration
**Objective**: Integrate all forensic-grade components seamlessly

**Tasks**:
1. **Update existing components**:
   - Modify `aiPipeline.ts` to use new scoring framework
   - Update `embeddings.ts` with evidence tracking
   - Enhance `rateLimitedQueue.ts` with audit capabilities
   - Refactor `query.ts` API to return audit trails

2. **Performance optimization**:
   - Caching strategies for expensive evidence lookups
   - Parallel processing for scoring criteria
   - Efficient storage for audit trails
   - Load balancing for dual AI models

#### 6.2 Production Features
**Objective**: Make system ready for professional forensic use

**Tasks**:
1. **Add professional features**:
   - Export recommendations to forensic report formats
   - Integration with existing forensic workflows
   - Batch processing for multiple scenarios
   - API endpoints for external tool integration

2. **Implement monitoring & maintenance**:
   - Health checks for all system components
   - Performance monitoring for response times
   - Error tracking and alerting
   - Automatic system updates for new evidence

## Technical Implementation Guidelines

### Configuration Management
- Use YAML files for human-readable configuration
- Implement JSON Schema validation for all config files
- Support environment variable overrides
- Hot-reload for development, restart for production changes

### AI Model Routing Strategy
```typescript
// Task Classification for Model Selection
const AI_TASK_ROUTING = {
  strategic: ['tool-selection', 'bias-analysis', 'methodology-decisions'],
  content: ['descriptions', 'explanations', 'micro-tasks', 'workflows']
};

// Cost Optimization Logic
if (taskComplexity === 'high' && responseTokens < 500) {
  useModel = 'large';
} else if (taskComplexity === 'low' && responseTokens > 1000) {
  useModel = 'small';
} else {
  useModel = config.defaultModel;
}
```

### Evidence Database Structure
```typescript
interface EvidenceSource {
  type: 'standard' | 'paper' | 'case-law' | 'expert-survey';
  citation: string;
  reliability: number;
  lastValidated: Date;
  content: string;
  metadata: Record<string, any>;
}
```

### Quality Assurance Requirements
- All scoring criteria must have documented methodologies
- Every recommendation must include confidence levels
- All AI-generated content must be marked as such
- Reproducibility tests must pass with >95% consistency
- Expert validation rate must exceed 80% for production use

## Success Metrics

### Forensic Quality Metrics
- **Transparency**: 100% of decisions traceable to evidence
- **Objectivity**: <5% variance in scoring between runs
- **Reproducibility**: >95% identical results for identical inputs
- **Expert Agreement**: >80% expert validation rate
- **Bias Reduction**: <10% bias score across all categories

### Performance Metrics  
- **Response Time**: <30 seconds for workflow recommendations
- **Accuracy**: >90% real-world case validation success
- **Coverage**: Support for >95% of common forensic scenarios
- **Reliability**: <1% system error rate
- **Cost Efficiency**: <50% cost reduction vs. single large model

## Risk Mitigation

### Technical Risks
- **AI Model Failures**: Implement robust fallback mechanisms
- **Configuration Errors**: Comprehensive validation and testing
- **Performance Issues**: Load testing and optimization
- **Data Corruption**: Backup and recovery procedures

### Forensic Risks
- **Bias Introduction**: Continuous monitoring and expert validation
- **Methodology Errors**: Peer review and scientific validation
- **Legal Challenges**: Ensure compliance with admissibility standards
- **Expert Disagreement**: Transparent uncertainty communication