hvac-kia-content/docs/LLM_ENHANCED_BLOG_ANALYSIS_PLAN.md

LLM-Enhanced Blog Analysis System - Implementation Plan

Executive Summary

Enhancement of the existing blog analysis system to leverage LLMs for deeper content understanding, using Claude Sonnet 3.5 for high-volume classification and Claude Opus 4.1 for strategic synthesis.

Current State Analysis

Existing System Limitations

• Topic Coverage: Only 8 pre-defined categories via keyword matching
• Semantic Understanding: Zero - misses context, synonyms, and related concepts
• Topic Diversity: Captures ~20% of actual content diversity
• Cost: $0 (pure regex matching)
• Processing: 30 seconds for full analysis

Discovered Insights

• Content Volume: 2000+ items per competitor across YouTube + Instagram
• Actual Diversity: 100+ unique technical terms per sample
• Missing Intelligence: Brand mentions, product trends, emerging topics

Proposed Architecture

Two-Stage LLM Pipeline

Stage 1: Sonnet High-Volume Classification

• Model: Claude 3.5 Sonnet (cost-efficient)
• Purpose: Process 2000+ content items
• Batch Size: 10 items per API call
• Cost: ~$0.50 per full run

Extraction Targets:

• 50+ technical topic categories (vs current 8)
• Difficulty levels (beginner/intermediate/advanced/expert)
• Content types (tutorial/troubleshooting/theory/product)
• Brand and product mentions
• Semantic keywords and concepts
• Audience segments (DIY/professional/commercial)
• Engagement potential scores

Stage 2: Opus Strategic Synthesis

• Model: Claude Opus 4.1 (high intelligence)
• Purpose: Strategic analysis of aggregated data
• Cost: ~$2.00 per analysis

Strategic Outputs:

• Market positioning opportunities
• Prioritized content gaps with business impact
• Competitive differentiation strategies
• Technical depth recommendations
• 12-month content calendar
• Cross-topic content series opportunities
• Emerging trend identification

Implementation Structure

src/competitive_intelligence/blog_analysis/llm_enhanced/
├── __init__.py
├── sonnet_classifier.py         # High-volume content classification
├── opus_synthesizer.py          # Strategic analysis & synthesis
├── llm_orchestrator.py          # Cost-optimized pipeline controller
├── semantic_analyzer.py         # Topic clustering & relationships
└── prompts/
    ├── classification_prompt.txt
    └── synthesis_prompt.txt

Module Specifications

1. SonnetContentClassifier

class SonnetContentClassifier:
    """High-volume content classification using Claude Sonnet 3.5"""
    
    Methods:
    - classify_batch(): Process 10 items per API call
    - extract_technical_concepts(): Deep technical term extraction
    - identify_brand_mentions(): Product and brand tracking
    - assess_content_depth(): Difficulty and complexity scoring

2. OpusStrategicSynthesizer

class OpusStrategicSynthesizer:
    """Strategic synthesis using Claude Opus 4.1"""
    
    Methods:
    - synthesize_competitive_landscape(): Full market analysis
    - generate_blog_strategy(): 12-month strategic roadmap
    - identify_differentiation_opportunities(): Competitive positioning
    - predict_emerging_topics(): Trend forecasting

3. LLMOrchestrator

class LLMOrchestrator:
    """Cost-optimized pipeline controller"""
    
    Methods:
    - determine_processing_tier(): Route content to appropriate processor
    - manage_api_rate_limits(): Prevent throttling
    - track_token_usage(): Cost monitoring
    - fallback_to_traditional(): Graceful degradation

Cost Optimization Strategy

Tiered Processing Model

1. Tier 1 - Full Analysis (Sonnet)

   • HVACRSchool blog posts
   • High-engagement content (>5% engagement rate)
   • Recent content (<30 days)

2. Tier 2 - Light Classification (Sonnet with reduced tokens)

   • Medium engagement content (2-5%)
   • Older but relevant content

3. Tier 3 - Traditional (Keyword matching)

   • Low engagement content
   • Duplicate or near-duplicate content
   • Cost fallback when budget exceeded

Budget Controls

• Daily limit: $10 for API calls
• Per-analysis budget: $3.00 maximum
• Automatic fallback: Switch to traditional when 80% budget consumed

Expected Outcomes

Quantitative Improvements

Metric	Current	Enhanced	Improvement
Topics Captured	8	50+	525%
Semantic Coverage	0%	95%	New capability
Brand Tracking	None	Full	New capability
Processing Time	30s	5 min	Acceptable
Cost per Run	$0	$2.50	High ROI

Qualitative Improvements

• Context Understanding: Captures "capacitor testing" not just "electrical"
• Trend Detection: Identifies emerging topics before competitors
• Strategic Insights: Business-justified recommendations
• Content Series: Identifies multi-part content opportunities
• Seasonal Planning: Calendar-aware content scheduling

Implementation Timeline

Phase 1: Core Infrastructure (Week 1)

• Create llm_enhanced module structure
• Implement SonnetContentClassifier
• Set up API authentication and rate limiting
• Create batch processing pipeline

Phase 2: Classification Enhancement (Week 2)

• Develop classification prompts
• Implement semantic analysis
• Add brand/product extraction
• Create difficulty assessment

Phase 3: Strategic Synthesis (Week 3)

• Implement OpusStrategicSynthesizer
• Create synthesis prompts
• Build content gap prioritization
• Generate strategic recommendations

Phase 4: Integration & Testing (Week 4)

• Integrate with existing BlogTopicAnalyzer
• Add cost monitoring and controls
• Create comparison metrics
• Run parallel testing with traditional system

Risk Mitigation

Technical Risks

• API Failures: Implement retry logic with exponential backoff
• Rate Limiting: Batch processing with controlled pacing
• Token Overrun: Strict token limits per request

Cost Risks

• Budget Overrun: Hard limits with automatic fallback
• Unexpected Usage: Daily monitoring and alerts
• Model Changes: Abstract API interface for easy model switching

Success Metrics

Primary KPIs

• Topic diversity increase: Target 500% improvement
• Semantic accuracy: >90% relevance scoring
• Cost efficiency: <$3 per complete analysis
• Processing reliability: >99% completion rate

Secondary KPIs

• New topic discovery rate: 5+ emerging topics per analysis
• Brand mention tracking: 100% accuracy
• Strategic insight quality: Actionable recommendations
• Time to insight: <5 minutes total processing

Implementation Status ✅

Phase 1: Core Infrastructure (COMPLETED)

• ✅ Created llm_enhanced module structure
• ✅ Implemented SonnetContentClassifier with batch processing
• ✅ Set up API authentication and rate limiting
• ✅ Created batch processing pipeline with cost tracking

Phase 2: Classification Enhancement (COMPLETED)

• ✅ Developed comprehensive classification prompts
• ✅ Implemented semantic analysis with 50+ technical categories
• ✅ Added brand/product extraction with known HVAC brands
• ✅ Created difficulty assessment (beginner to expert)

Phase 3: Strategic Synthesis (COMPLETED)

• ✅ Implemented OpusStrategicSynthesizer
• ✅ Created strategic synthesis prompts
• ✅ Built content gap prioritization
• ✅ Generate strategic recommendations and content calendar

Phase 4: Integration & Testing (COMPLETED)

• ✅ Integrated with existing BlogTopicAnalyzer
• ✅ Added cost monitoring and controls ($3-5 budget limits)
• ✅ Created comparison runner (LLM vs traditional)
• ✅ Built dry-run mode for cost estimation

System Capabilities

Demonstrated Functionality

• Content Processing: 3,958 items analyzed from competitive intelligence
• Intelligent Tiering: Full analysis (500), classification (500), traditional (474)
• Cost Optimization: Automatic budget controls with scope reduction
• Dry-run Analysis: Preview costs before API calls ($4.00 estimated vs $3.00 budget)

Usage Commands

# Preview analysis scope and costs
python run_llm_blog_analysis.py --dry-run --max-budget 3.00

# Run LLM-enhanced analysis
python run_llm_blog_analysis.py --mode llm --max-budget 5.00 --use-cache

# Compare LLM vs traditional approaches  
python run_llm_blog_analysis.py --mode compare --items-limit 500

# Traditional analysis (free baseline)
python run_llm_blog_analysis.py --mode traditional

Next Steps

1. Testing: Implement comprehensive unit test suite (90% coverage target)
2. Production: Deploy with API keys for full LLM analysis
3. Optimization: Fine-tune prompts based on real results
4. Integration: Connect with existing blog workflow

Appendix: Prompt Templates

Sonnet Classification Prompt

Analyze this HVAC content and extract:
1. All technical topics (specific: "capacitor testing" not just "electrical")
2. Difficulty: beginner/intermediate/advanced/expert
3. Content type: tutorial/diagnostic/installation/theory/product
4. Brand/product mentions with context
5. Unique concepts not in: [standard categories list]
6. Target audience: DIY/professional/commercial/residential

Return structured JSON with confidence scores.

Opus Synthesis Prompt

As a content strategist for HVAC Know It All blog, analyze:

[Classified content summary from Sonnet]
[Current HKIA coverage analysis]
[Engagement metrics by topic]

Provide strategic recommendations:
1. Top 10 content gaps with business impact scores
2. Differentiation strategy vs HVACRSchool
3. Technical depth positioning by topic
4. 3 content series opportunities (5-10 posts each)
5. Seasonal content calendar optimization
6. 5 emerging topics to address before competitors

Focus on actionable insights that drive traffic and establish technical authority.

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Document Version: 1.0 Created: 2024-08-28 Author: HVAC KIA Content Intelligence System