forensic-pathways/RAG-Roadmap.md

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):

   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):

   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

// 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

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