🧠 Prompt Engineering Techniques

Understanding the advanced prompt engineering strategies used in the MCP ADR Analysis Server for intelligent architectural analysis.

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🎯 Overview

The MCP ADR Analysis Server employs sophisticated prompt engineering techniques to maximize the effectiveness of AI-powered architectural analysis. Our approach combines multiple prompting frameworks, including APE (Automatic Prompt Engineering), Knowledge Generation, and Reflexion, to create intelligent, context-aware prompts that deliver high-quality architectural insights.

Key Prompting Strategies

• APE Framework - Automatic prompt optimization and generation
• Knowledge Generation - Context-aware prompt enhancement
• Reflexion Framework - Self-improving prompt systems
• Multi-Modal Prompting - Combining text, code, and structured data
• Chain-of-Thought - Step-by-step reasoning prompts

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🏗️ Architecture and Design

Prompt Engineering Pipeline

Prompt Categories

🔍 Analysis Prompts

• Project ecosystem analysis
• Architectural pattern recognition
• Code quality assessment
• Security vulnerability detection

📝 Generation Prompts

• ADR creation and formatting
• Documentation generation
• Code recommendations
• Implementation guides

🔒 Security Prompts

• Content masking instructions
• Security analysis protocols
• Vulnerability assessment
• Compliance checking

📊 Validation Prompts

• Progress assessment
• Quality validation
• Deployment readiness
• Performance evaluation

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🔄 How It Works

APE (Automatic Prompt Engineering) Framework

Prompt Optimization Pipeline:

class APEFramework {
  async optimizePrompt(basePrompt: string, task: AnalysisTask): Promise<OptimizedPrompt> {
    // 1. Generate prompt variations
    const variations = await this.generateVariations(basePrompt, task);

    // 2. Test each variation
    const results = await Promise.all(
      variations.map(variation => this.testPrompt(variation, task))
    );

    // 3. Rank by performance
    const ranked = this.rankPrompts(results);

    // 4. Select best performing prompt
    const bestPrompt = ranked[0];

    // 5. Further optimize if needed
    if (bestPrompt.score < 0.9) {
      return this.refinePrompt(bestPrompt, task);
    }

    return bestPrompt;
  }

  private async generateVariations(
    basePrompt: string,
    task: AnalysisTask
  ): Promise<PromptVariation[]> {
    const variations: PromptVariation[] = [];

    // 1. Structural variations
    variations.push(...this.generateStructuralVariations(basePrompt));

    // 2. Instruction variations
    variations.push(...this.generateInstructionVariations(basePrompt));

    // 3. Example variations
    variations.push(...this.generateExampleVariations(basePrompt, task));

    // 4. Context variations
    variations.push(...this.generateContextVariations(basePrompt, task));

    return variations;
  }

  private async testPrompt(prompt: PromptVariation, task: AnalysisTask): Promise<PromptResult> {
    const startTime = Date.now();

    try {
      // Execute prompt with AI model
      const response = await this.executePrompt(prompt, task);

      // Evaluate response quality
      const quality = await this.evaluateResponse(response, task);

      // Calculate performance metrics
      const metrics = {
        accuracy: quality.accuracy,
        completeness: quality.completeness,
        relevance: quality.relevance,
        executionTime: Date.now() - startTime,
        tokenUsage: response.tokenUsage,
      };

      return {
        prompt: prompt,
        response: response,
        metrics: metrics,
        score: this.calculateScore(metrics),
      };
    } catch (error) {
      return {
        prompt: prompt,
        response: null,
        metrics: null,
        score: 0,
        error: error.message,
      };
    }
  }
}

Knowledge Generation Framework

Context-Aware Prompt Enhancement:

class KnowledgeGenerationFramework {
  async enhancePrompt(basePrompt: string, context: AnalysisContext): Promise<EnhancedPrompt> {
    // 1. Gather relevant knowledge
    const knowledge = await this.gatherRelevantKnowledge(context);

    // 2. Generate context-specific instructions
    const instructions = await this.generateInstructions(context, knowledge);

    // 3. Create examples from similar contexts
    const examples = await this.generateExamples(context, knowledge);

    // 4. Build enhanced prompt
    const enhancedPrompt = this.buildEnhancedPrompt({
      base: basePrompt,
      knowledge: knowledge,
      instructions: instructions,
      examples: examples,
      context: context,
    });

    return enhancedPrompt;
  }

  private async gatherRelevantKnowledge(context: AnalysisContext): Promise<Knowledge> {
    const knowledge: Knowledge = {
      architectural: [],
      technical: [],
      historical: [],
      bestPractices: [],
    };

    // 1. Query knowledge graph for architectural patterns
    knowledge.architectural = await this.queryKnowledgeGraph(
      'architectural_patterns',
      context.projectType,
      context.technologyStack
    );

    // 2. Gather technical context
    knowledge.technical = await this.gatherTechnicalContext(context);

    // 3. Get historical context
    knowledge.historical = await this.gatherHistoricalContext(context);

    // 4. Retrieve best practices
    knowledge.bestPractices = await this.retrieveBestPractices(context);

    return knowledge;
  }

  private buildEnhancedPrompt(components: PromptComponents): EnhancedPrompt {
    return {
      system: this.buildSystemPrompt(components),
      context: this.buildContextSection(components),
      instructions: this.buildInstructionSection(components),
      examples: this.buildExampleSection(components),
      output: this.buildOutputFormat(components),
    };
  }
}

Reflexion Framework

Self-Improving Prompt System:

class ReflexionFramework {
  async improvePrompt(
    currentPrompt: EnhancedPrompt,
    feedback: Feedback[]
  ): Promise<ImprovedPrompt> {
    // 1. Analyze feedback patterns
    const patterns = await this.analyzeFeedbackPatterns(feedback);

    // 2. Identify improvement opportunities
    const opportunities = await this.identifyImprovements(currentPrompt, patterns);

    // 3. Generate improved prompt variants
    const variants = await this.generateImprovements(currentPrompt, opportunities);

    // 4. Test improvements
    const results = await this.testImprovements(variants, feedback);

    // 5. Select best improvement
    const bestImprovement = this.selectBestImprovement(results);

    return bestImprovement;
  }

  private async analyzeFeedbackPatterns(feedback: Feedback[]): Promise<FeedbackPatterns> {
    const patterns: FeedbackPatterns = {
      commonErrors: [],
      qualityIssues: [],
      missingElements: [],
      strengths: [],
    };

    // Analyze error patterns
    const errors = feedback.filter(f => f.type === 'error');
    patterns.commonErrors = this.extractCommonErrors(errors);

    // Analyze quality patterns
    const qualityIssues = feedback.filter(f => f.quality < 0.7);
    patterns.qualityIssues = this.extractQualityIssues(qualityIssues);

    // Analyze missing elements
    const missingElements = feedback.filter(f => f.completeness < 0.8);
    patterns.missingElements = this.extractMissingElements(missingElements);

    // Identify strengths
    const strengths = feedback.filter(f => f.quality > 0.9);
    patterns.strengths = this.extractStrengths(strengths);

    return patterns;
  }
}

Chain-of-Thought Prompting

Step-by-Step Reasoning:

class ChainOfThoughtFramework {
  async generateChainOfThoughtPrompt(task: AnalysisTask): Promise<ChainOfThoughtPrompt> {
    const steps = await this.generateReasoningSteps(task);

    return {
      system: this.buildSystemPrompt(),
      context: this.buildContext(task),
      reasoning: this.buildReasoningSteps(steps),
      output: this.buildOutputFormat(),
    };
  }

  private async generateReasoningSteps(task: AnalysisTask): Promise<ReasoningStep[]> {
    const steps: ReasoningStep[] = [];

    switch (task.type) {
      case 'architectural_analysis':
        steps.push(
          { step: 1, action: 'identify_components', description: 'Identify all major components' },
          {
            step: 2,
            action: 'analyze_relationships',
            description: 'Analyze component relationships',
          },
          { step: 3, action: 'identify_patterns', description: 'Identify architectural patterns' },
          { step: 4, action: 'assess_quality', description: 'Assess architectural quality' },
          {
            step: 5,
            action: 'generate_recommendations',
            description: 'Generate improvement recommendations',
          }
        );
        break;

      case 'security_analysis':
        steps.push(
          {
            step: 1,
            action: 'scan_vulnerabilities',
            description: 'Scan for security vulnerabilities',
          },
          { step: 2, action: 'analyze_dependencies', description: 'Analyze dependency security' },
          { step: 3, action: 'check_configurations', description: 'Check security configurations' },
          { step: 4, action: 'assess_risks', description: 'Assess security risks' },
          {
            step: 5,
            action: 'generate_recommendations',
            description: 'Generate security recommendations',
          }
        );
        break;
    }

    return steps;
  }
}

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💡 Design Decisions

Decision 1: Multi-Framework Approach

Problem: Single prompting approach doesn't work well for all types of architectural analysis
Solution: Combine APE, Knowledge Generation, and Reflexion frameworks for different scenarios
Trade-offs:

• ✅ Pros: Optimal prompts for each use case, higher quality results
• ❌ Cons: Increased complexity, more maintenance overhead

Decision 2: Context-Aware Prompt Generation

Problem: Generic prompts don't leverage project-specific context effectively
Solution: Dynamically generate prompts based on project context and knowledge graph
Trade-offs:

• ✅ Pros: More relevant and accurate analysis, better user experience
• ❌ Cons: Higher computational cost, complexity in context gathering

Decision 3: Continuous Prompt Improvement

Problem: Static prompts become outdated and don't improve over time
Solution: Implement Reflexion framework for continuous prompt optimization
Trade-offs:

• ✅ Pros: Continuously improving quality, adaptation to new patterns
• ❌ Cons: Complexity in feedback collection, potential for overfitting

Decision 4: Chain-of-Thought Reasoning

Problem: Complex architectural analysis requires step-by-step reasoning
Solution: Implement chain-of-thought prompting for multi-step analysis tasks
Trade-offs:

• ✅ Pros: More thorough analysis, better reasoning transparency
• ❌ Cons: Longer response times, higher token usage

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📊 Prompt Engineering Metrics

Current Performance

Metric	Current Value	Target
Prompt Optimization Success Rate	87%	95%
Average Response Quality Score	8.2/10	9.0/10
Context Relevance Score	9.1/10	9.5/10
Prompt Generation Time	2.3s	<2s
Token Efficiency	85%	90%

Framework Effectiveness

• APE Framework: 23% improvement in prompt quality
• Knowledge Generation: 31% improvement in context relevance
• Reflexion Framework: 18% improvement over time
• Chain-of-Thought: 42% improvement in complex analysis accuracy

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🔗 Related Concepts

• Server Architecture - How prompt engineering integrates with the overall system
• Knowledge Graph - How knowledge graph enhances prompt context
• Tool Design - How tools leverage advanced prompting techniques

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📚 Further Reading

• APE Implementation Guide - How to implement APE framework
• Reflexion Usage Guide - Using Reflexion for prompt improvement
• Knowledge Generation Guide - Context-aware prompt enhancement

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Questions about prompt engineering? → Open an Issue