mcp-adr-analysis-server

# ADR-002: AI Integration and Advanced Prompting Strategy ## Status Accepted ## Context The MCP ADR Analysis Server integrates advanced AI capabilities for architectural analysis, including Knowledge Generation, Reflexion learning, and Automatic Prompt Engineering (APE). The system needs to provide high-quality analysis with confidence scoring, evidence-based recommendations, and systematic verification processes. The choice of AI integration approach affects analysis quality, response time, system complexity, and reliability. ## Decision We will implement a hybrid AI integration approach using advanced prompting techniques (Knowledge Generation + Reflexion + APE) with external AI services, combined with local caching and methodological pragmatism framework for systematic verification and confidence scoring. Key components: - **Knowledge Generation**: Domain-specific architectural knowledge enhancement - **Reflexion Learning**: Learning from past analysis outcomes and experiences - **Automatic Prompt Engineering**: Optimized prompt generation for better results - **Confidence Scoring**: Systematic confidence assessment for all recommendations - **Evidence-Based Analysis**: All recommendations backed by concrete evidence - **Methodological Pragmatism**: Explicit fallibilism and systematic verification ## Consequences **Positive:** - Enhanced analysis quality with confidence scoring and evidence backing - Systematic verification processes reduce false positives - Learning from past experiences improves future analysis - Methodological pragmatism provides structured approach to uncertainty - Advanced prompting techniques improve AI response quality - Local caching reduces latency and external service dependency **Negative:** - Increased complexity in prompt management and AI workflow orchestration - Dependency on external AI services for advanced analysis - Potential latency in analysis due to multi-step AI processing - Need for sophisticated error handling and fallback mechanisms - Higher computational costs due to advanced prompting techniques - Complexity in managing confidence scoring and evidence validation