RAG Roadmap
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RAG-Roadmap.md
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RAG-Roadmap.md
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# Forensic-Grade RAG Implementation Roadmap
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## Context & Current State Analysis
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You have access to a forensic tools recommendation system built with:
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- **Embeddings-based retrieval** (src/utils/embeddings.ts)
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- **Multi-stage AI pipeline** (src/utils/aiPipeline.ts)
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- **Micro-task processing** for detailed analysis
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- **Rate limiting and queue management** (src/utils/rateLimitedQueue.ts)
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- **YAML-based tool database** (src/data/tools.yaml)
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**Current Architecture**: Basic RAG (Retrieve → AI Selection → Micro-task Generation)
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**Target Architecture**: Forensic-Grade RAG with transparency, objectivity, and reproducibility
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## Implementation Roadmap
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### PHASE 1: Configuration Externalization & AI Architecture Enhancement (Weeks 1-2)
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#### 1.1 Complete Configuration Externalization
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**Objective**: Remove all hard-coded values from codebase (except AI prompts)
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**Tasks**:
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1. **Create comprehensive configuration schema** in `src/config/`
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- `forensic-scoring.yaml` - All scoring criteria, weights, thresholds
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- `ai-models.yaml` - AI model configurations and routing
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- `system-parameters.yaml` - Rate limits, queue settings, processing parameters
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- `validation-criteria.yaml` - Expert validation rules, bias detection parameters
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2. **Implement configuration loader** (`src/utils/configLoader.ts`)
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- Hot-reload capability for configuration changes
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- Environment-specific overrides (dev/staging/prod)
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- Configuration validation and schema enforcement
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- Default fallbacks for missing values
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3. **Audit existing codebase** for hard-coded values:
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- Search for literal numbers, strings, arrays in TypeScript files
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- Extract to configuration files with meaningful names
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- Ensure all thresholds (similarity scores, rate limits, token counts) are configurable
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#### 1.2 Dual AI Model Architecture Implementation
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**Objective**: Implement large + small model strategy for optimal cost/performance
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**Tasks**:
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1. **Extend environment configuration**:
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```
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# Strategic Analysis Model (Large, Few Tokens)
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AI_STRATEGIC_ENDPOINT=
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AI_STRATEGIC_API_KEY=
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AI_STRATEGIC_MODEL=mistral-large-latest
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AI_STRATEGIC_MAX_TOKENS=500
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AI_STRATEGIC_CONTEXT_WINDOW=32000
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# Content Generation Model (Small, Many Tokens)
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AI_CONTENT_ENDPOINT=
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AI_CONTENT_API_KEY=
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AI_CONTENT_MODEL=mistral-small-latest
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AI_CONTENT_MAX_TOKENS=2000
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AI_CONTENT_CONTEXT_WINDOW=8000
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```
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2. **Create AI router** (`src/utils/aiRouter.ts`):
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- Route different task types to appropriate models
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- **Strategic tasks** → Large model: tool selection, bias analysis, methodology decisions
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- **Content tasks** → Small model: descriptions, explanations, micro-task outputs
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- Automatic fallback logic if primary model fails
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- Usage tracking and cost optimization
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3. **Update aiPipeline.ts**:
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- Replace single `callAI()` method with task-specific methods
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- Implement intelligent routing based on task complexity
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- Add token estimation for optimal model selection
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### PHASE 2: Evidence-Based Scoring Framework (Weeks 3-5)
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#### 2.1 Forensic Scoring Engine Implementation
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**Objective**: Replace subjective AI selection with objective, measurable criteria
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**Tasks**:
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1. **Create scoring framework** (`src/scoring/ForensicScorer.ts`):
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```typescript
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interface ScoringCriterion {
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name: string;
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weight: number;
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methodology: string;
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dataSources: string[];
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calculator: (tool: Tool, scenario: Scenario) => Promise<CriterionScore>;
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}
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interface CriterionScore {
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value: number; // 0-100
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confidence: number; // 0-100
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evidence: Evidence[];
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lastUpdated: Date;
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}
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```
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2. **Implement core scoring criteria**:
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- **Court Admissibility Scorer**: Based on legal precedent database
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- **Scientific Validity Scorer**: Based on peer-reviewed research citations
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- **Methodology Alignment Scorer**: NIST SP 800-86 compliance assessment
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- **Expert Consensus Scorer**: Practitioner survey data integration
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- **Error Rate Scorer**: Known false positive/negative rates
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3. **Build evidence provenance system**:
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- Track source of every score component
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- Maintain citation database for all claims
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- Version control for scoring methodologies
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- Automatic staleness detection for outdated evidence
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#### 2.2 Deterministic Core Implementation
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**Objective**: Ensure reproducible results for identical inputs
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**Tasks**:
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1. **Implement deterministic pipeline** (`src/analysis/DeterministicAnalyzer.ts`):
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- Rule-based scenario classification (SCADA/Mobile/Network/etc.)
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- Mathematical scoring combination (weighted averages, not AI decisions)
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- Consistent tool ranking algorithms
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- Reproducibility validation tests
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2. **Add AI enhancement layer**:
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- AI provides explanations, NOT decisions
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- AI generates workflow descriptions based on deterministic selections
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- AI creates contextual advice around objective tool choices
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### PHASE 3: Transparency & Audit Trail System (Weeks 4-6)
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#### 3.1 Complete Audit Trail Implementation
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**Objective**: Track every decision with forensic-grade documentation
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**Tasks**:
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1. **Create audit framework** (`src/audit/AuditTrail.ts`):
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```typescript
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interface ForensicAuditTrail {
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queryId: string;
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userQuery: string;
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processingSteps: AuditStep[];
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finalRecommendation: RecommendationWithEvidence;
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reproducibilityHash: string;
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validationStatus: ValidationStatus;
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}
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interface AuditStep {
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stepName: string;
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input: any;
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methodology: string;
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output: any;
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evidence: Evidence[];
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confidence: number;
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processingTime: number;
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modelUsed?: string;
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}
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```
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2. **Implement evidence citation system**:
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- Automatic citation generation for all claims
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- Link to source standards (NIST, ISO, RFC)
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- Reference scientific papers for methodology choices
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- Track expert validation contributors
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3. **Build explanation generator**:
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- Human-readable reasoning for every recommendation
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- "Why this tool" and "Why not alternatives" explanations
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- Confidence level communication
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- Uncertainty quantification
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#### 3.2 Bias Detection & Mitigation System
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**Objective**: Actively detect and correct recommendation biases
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**Tasks**:
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1. **Implement bias detection** (`src/bias/BiasDetector.ts`):
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- **Popularity bias**: Over-recommendation of well-known tools
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- **Availability bias**: Preference for easily accessible tools
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- **Recency bias**: Over-weighting of newest tools
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- **Cultural bias**: Platform or methodology preferences
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2. **Create mitigation strategies**:
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- Automatic bias adjustment algorithms
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- Diversity requirements for recommendations
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- Fairness metrics across tool categories
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- Bias reporting in audit trails
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### PHASE 4: Expert Validation & Learning System (Weeks 6-8)
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#### 4.1 Expert Review Integration
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**Objective**: Enable forensic experts to validate and improve recommendations
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**Tasks**:
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1. **Build expert validation interface** (`src/validation/ExpertReview.ts`):
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- Structured feedback collection from forensic practitioners
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- Agreement/disagreement tracking with detailed reasoning
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- Expert consensus building over time
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- Minority opinion preservation
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2. **Implement validation loop**:
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- Flag recommendations requiring expert review
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- Track expert validation rates and patterns
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- Update scoring based on real-world feedback
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- Methodology improvement based on expert input
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#### 4.2 Real-World Case Learning
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**Objective**: Learn from actual forensic investigations
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**Tasks**:
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1. **Create case study integration** (`src/learning/CaseStudyLearner.ts`):
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- Anonymous case outcome tracking
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- Tool effectiveness measurement in real scenarios
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- Methodology success/failure analysis
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- Continuous improvement based on field results
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2. **Implement feedback loops**:
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- Post-case recommendation validation
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- Tool performance tracking in actual investigations
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- Methodology refinement based on outcomes
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- Success rate improvement over time
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### PHASE 5: Advanced Features & Scientific Rigor (Weeks 7-10)
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#### 5.1 Confidence & Uncertainty Quantification
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**Objective**: Provide scientific confidence levels for all recommendations
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**Tasks**:
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1. **Implement uncertainty quantification** (`src/uncertainty/ConfidenceCalculator.ts`):
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- Statistical confidence intervals for scores
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- Uncertainty propagation through scoring pipeline
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- Risk assessment for recommendation reliability
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- Alternative recommendation ranking
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2. **Add fallback recommendation system**:
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- Multiple ranked alternatives for each recommendation
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- Contingency planning for tool failures
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- Risk-based recommendation portfolios
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- Sensitivity analysis for critical decisions
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#### 5.2 Reproducibility Testing Framework
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**Objective**: Ensure consistent results across time and implementations
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**Tasks**:
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1. **Build reproducibility testing** (`src/testing/ReproducibilityTester.ts`):
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- Automated consistency validation
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- Inter-rater reliability testing
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- Cross-temporal stability analysis
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- Version control for methodology changes
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2. **Implement quality assurance**:
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- Continuous integration for reproducibility
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- Regression testing for methodology changes
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- Performance monitoring for consistency
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- Alert system for unexpected variations
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### PHASE 6: Integration & Production Readiness (Weeks 9-12)
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#### 6.1 System Integration
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**Objective**: Integrate all forensic-grade components seamlessly
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**Tasks**:
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1. **Update existing components**:
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- Modify `aiPipeline.ts` to use new scoring framework
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- Update `embeddings.ts` with evidence tracking
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- Enhance `rateLimitedQueue.ts` with audit capabilities
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- Refactor `query.ts` API to return audit trails
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2. **Performance optimization**:
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- Caching strategies for expensive evidence lookups
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- Parallel processing for scoring criteria
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- Efficient storage for audit trails
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- Load balancing for dual AI models
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#### 6.2 Production Features
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**Objective**: Make system ready for professional forensic use
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**Tasks**:
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1. **Add professional features**:
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- Export recommendations to forensic report formats
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- Integration with existing forensic workflows
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- Batch processing for multiple scenarios
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- API endpoints for external tool integration
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2. **Implement monitoring & maintenance**:
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- Health checks for all system components
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- Performance monitoring for response times
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- Error tracking and alerting
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- Automatic system updates for new evidence
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## Technical Implementation Guidelines
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### Configuration Management
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- Use YAML files for human-readable configuration
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- Implement JSON Schema validation for all config files
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- Support environment variable overrides
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- Hot-reload for development, restart for production changes
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### AI Model Routing Strategy
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```typescript
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// Task Classification for Model Selection
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const AI_TASK_ROUTING = {
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strategic: ['tool-selection', 'bias-analysis', 'methodology-decisions'],
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content: ['descriptions', 'explanations', 'micro-tasks', 'workflows']
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};
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// Cost Optimization Logic
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if (taskComplexity === 'high' && responseTokens < 500) {
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useModel = 'large';
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} else if (taskComplexity === 'low' && responseTokens > 1000) {
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useModel = 'small';
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} else {
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useModel = config.defaultModel;
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}
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```
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### Evidence Database Structure
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```typescript
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interface EvidenceSource {
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type: 'standard' | 'paper' | 'case-law' | 'expert-survey';
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citation: string;
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reliability: number;
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lastValidated: Date;
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content: string;
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metadata: Record<string, any>;
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}
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```
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### Quality Assurance Requirements
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- All scoring criteria must have documented methodologies
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- Every recommendation must include confidence levels
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- All AI-generated content must be marked as such
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- Reproducibility tests must pass with >95% consistency
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- Expert validation rate must exceed 80% for production use
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## Success Metrics
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### Forensic Quality Metrics
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- **Transparency**: 100% of decisions traceable to evidence
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- **Objectivity**: <5% variance in scoring between runs
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- **Reproducibility**: >95% identical results for identical inputs
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- **Expert Agreement**: >80% expert validation rate
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- **Bias Reduction**: <10% bias score across all categories
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### Performance Metrics
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- **Response Time**: <30 seconds for workflow recommendations
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- **Accuracy**: >90% real-world case validation success
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- **Coverage**: Support for >95% of common forensic scenarios
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- **Reliability**: <1% system error rate
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- **Cost Efficiency**: <50% cost reduction vs. single large model
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## Risk Mitigation
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### Technical Risks
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- **AI Model Failures**: Implement robust fallback mechanisms
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- **Configuration Errors**: Comprehensive validation and testing
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- **Performance Issues**: Load testing and optimization
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- **Data Corruption**: Backup and recovery procedures
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### Forensic Risks
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- **Bias Introduction**: Continuous monitoring and expert validation
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- **Methodology Errors**: Peer review and scientific validation
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- **Legal Challenges**: Ensure compliance with admissibility standards
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- **Expert Disagreement**: Transparent uncertainty communication
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