Knowledge Generation Framework Design

Overview

The Knowledge Generation framework implements advanced prompting techniques to generate domain-specific architectural knowledge before making decisions. This enhances the accuracy and context-awareness of all MCP ADR Analysis Server tools while maintaining the 100% prompt-driven architecture.

Core Concept

Knowledge Generation Prompting works by:

1. Domain Analysis: Identify the architectural domain and context
2. Knowledge Generation: Generate relevant domain-specific knowledge
3. Knowledge Integration: Combine generated knowledge with task prompts
4. Decision Making: Use enhanced prompts for better decision quality

Architecture Integration

Existing Components Integration

• PromptObject Interface: Knowledge generation returns PromptObject format
• combinePrompts(): Combines knowledge prompts with task prompts
• Cache System: Caches generated knowledge for performance
• Security Validation: Validates generated knowledge content

Framework Components

Knowledge Generation Framework
├── Domain Templates (Web, Mobile, Microservices, etc.)
├── Knowledge Generator (Core generation logic)
├── Knowledge Cache (Persistent knowledge storage)
├── Integration Layer (Tool integration utilities)
└── Configuration (Settings and customization)

Interface Definitions

Core Knowledge Interfaces

export interface DomainKnowledge {
  domain: ArchitecturalDomain;
  knowledge: KnowledgeItem[];
  confidence: number;
  timestamp: string;
  sources: string[];
  metadata: KnowledgeMetadata;
}

export interface KnowledgeItem {
  category: KnowledgeCategory;
  title: string;
  content: string;
  relevance: number;
  evidence: string[];
  tags: string[];
}

export interface KnowledgeGenerationConfig {
  domains: ArchitecturalDomain[];
  depth: 'basic' | 'intermediate' | 'advanced';
  cacheEnabled: boolean;
  cacheTTL: number;
  securityValidation: boolean;
  customTemplates?: DomainTemplate[];
}

export interface KnowledgeGenerationResult {
  knowledgePrompt: PromptObject;
  enhancedPrompt: PromptObject;
  cacheKey: string;
  metadata: GenerationMetadata;
}

Domain Categories

export type ArchitecturalDomain = 
  | 'web-applications'
  | 'mobile-applications' 
  | 'microservices'
  | 'database-design'
  | 'cloud-infrastructure'
  | 'devops-cicd'
  | 'security-patterns'
  | 'performance-optimization'
  | 'api-design'
  | 'data-architecture';

export type KnowledgeCategory =
  | 'best-practices'
  | 'design-patterns'
  | 'anti-patterns'
  | 'technology-specific'
  | 'performance-considerations'
  | 'security-guidelines'
  | 'scalability-patterns'
  | 'testing-strategies';

Domain Knowledge Templates

Web Applications Template

const webApplicationsTemplate: DomainTemplate = {
  domain: 'web-applications',
  categories: [
    {
      category: 'best-practices',
      items: [
        'Component-based architecture principles',
        'State management patterns (Redux, Zustand, Context)',
        'Performance optimization techniques',
        'Accessibility (a11y) guidelines',
        'SEO optimization strategies'
      ]
    },
    {
      category: 'design-patterns',
      items: [
        'Model-View-Controller (MVC)',
        'Component composition patterns',
        'Higher-Order Components (HOCs)',
        'Render props pattern',
        'Custom hooks pattern'
      ]
    }
  ]
};

Microservices Template

const microservicesTemplate: DomainTemplate = {
  domain: 'microservices',
  categories: [
    {
      category: 'best-practices',
      items: [
        'Service decomposition strategies',
        'API gateway patterns',
        'Service mesh architecture',
        'Event-driven communication',
        'Distributed data management'
      ]
    },
    {
      category: 'anti-patterns',
      items: [
        'Distributed monolith',
        'Chatty interfaces',
        'Shared databases',
        'Synchronous communication overuse'
      ]
    }
  ]
};

Core Functions

Primary Knowledge Generation Function

export async function generateArchitecturalKnowledge(
  context: ArchitecturalContext,
  config: KnowledgeGenerationConfig = {}
): Promise<KnowledgeGenerationResult>

Domain Detection Function

export async function detectArchitecturalDomains(
  projectContext: ProjectContext
): Promise<ArchitecturalDomain[]>

Knowledge Enhancement Function

export async function enhancePromptWithKnowledge(
  originalPrompt: PromptObject,
  domains: ArchitecturalDomain[],
  config: KnowledgeGenerationConfig = {}
): Promise<PromptObject>

Caching Strategy

Cache Key Generation

function generateKnowledgeCacheKey(
  domains: ArchitecturalDomain[],
  context: ArchitecturalContext,
  config: KnowledgeGenerationConfig
): string {
  return `knowledge:${domains.join('+')}-${hashContext(context)}-${hashConfig(config)}`;
}

Cache TTL Strategy

• Basic Knowledge: 24 hours (stable architectural principles)
• Technology-Specific: 6 hours (evolving best practices)
• Project-Specific: 1 hour (context-dependent knowledge)

Security Validation

Knowledge Content Validation

1. Source Verification: Ensure knowledge comes from trusted sources
2. Content Sanitization: Remove potentially harmful content
3. Relevance Validation: Verify knowledge relevance to context
4. Quality Assessment: Evaluate knowledge quality and accuracy

Security Checks

export interface KnowledgeSecurityCheck {
  contentSafety: boolean;
  sourceReliability: number;
  relevanceScore: number;
  qualityScore: number;
  warnings: string[];
}

Integration Patterns

Tool Integration Example

// In suggest_adrs tool
const knowledgeResult = await generateArchitecturalKnowledge(
  { domains: ['web-applications', 'api-design'], projectContext },
  { depth: 'intermediate', cacheEnabled: true }
);

const enhancedPrompt = combinePrompts(
  knowledgeResult.knowledgePrompt,
  originalAdrPrompt
);

Configuration Integration

export interface ToolKnowledgeConfig {
  enableKnowledgeGeneration: boolean;
  domains: ArchitecturalDomain[];
  knowledgeDepth: 'basic' | 'intermediate' | 'advanced';
  cacheStrategy: 'aggressive' | 'moderate' | 'minimal';
}

Usage Patterns

Pattern 1: Domain-Aware ADR Suggestions

1. Detect project domains from codebase analysis
2. Generate domain-specific architectural knowledge
3. Enhance ADR suggestion prompts with knowledge
4. Cache knowledge for subsequent suggestions

Pattern 2: Context-Enhanced Analysis

1. Analyze project structure and technologies
2. Generate relevant architectural knowledge
3. Combine knowledge with analysis prompts
4. Provide context-aware recommendations

Pattern 3: Knowledge-Driven Research Questions

1. Identify knowledge gaps in project context
2. Generate domain expertise knowledge
3. Create research questions based on knowledge
4. Enhance research planning with domain insights

Performance Considerations

Optimization Strategies

1. Lazy Loading: Load domain templates only when needed
2. Incremental Generation: Generate knowledge incrementally
3. Parallel Processing: Generate knowledge for multiple domains in parallel
4. Smart Caching: Cache at multiple levels (domain, context, config)

Resource Management

• Memory Usage: Limit concurrent knowledge generation
• Cache Size: Implement cache size limits and cleanup
• Generation Time: Set timeouts for knowledge generation
• Quality vs Speed: Balance knowledge depth with generation time

Future Enhancements

Planned Features

1. Custom Domain Templates: User-defined domain knowledge
2. Knowledge Learning: Learn from successful knowledge applications
3. Knowledge Validation: Validate knowledge against project outcomes
4. Knowledge Sharing: Share knowledge across projects and teams

Integration Opportunities

1. APE Integration: Optimize knowledge generation prompts
2. Reflexion Integration: Learn from knowledge effectiveness
3. External Sources: Integrate with external knowledge bases
4. Team Knowledge: Incorporate team-specific knowledge

Implementation Roadmap

Phase 1: Core Framework (Week 1-2)

• Implement basic knowledge generation
• Create domain templates
• Integrate with existing prompt system
• Add basic caching

Phase 2: Advanced Features (Week 3-4)

• Add security validation
• Implement configuration system
• Create tool integration utilities
• Add performance optimizations

Phase 3: Enhancement & Testing (Week 5-6)

• Comprehensive testing
• Performance tuning
• Documentation completion
• Integration with priority tools

This framework design provides a solid foundation for implementing Knowledge Generation across the MCP ADR Analysis Server while maintaining architectural consistency and security requirements.