# 🤖 AI Integration Plan for Content Planning System ## 📋 Current Status Analysis ### ❌ **Issues Identified** 1. **Hardcoded Values**: All AI services currently use simulated data instead of real AI calls 2. **Missing AI Integration**: No actual LLM calls in FastAPI services 3. **Unused AI Infrastructure**: Gemini provider exists but not integrated 4. **Missing AI Prompts**: Advanced prompts from legacy system not implemented ### ✅ **Available AI Infrastructure** 1. **Gemini Provider**: `backend/llm_providers/gemini_provider.py` ✅ 2. **Main Text Generation**: `backend/llm_providers/main_text_generation.py` ✅ 3. **API Key Management**: `backend/services/api_key_manager.py` ✅ 4. **AI Prompts**: Available in `CONTENT_GAP_ANALYSIS_DEEP_DIVE.md` ✅ ## 🎯 **AI Integration Strategy** ### **Phase 1: Core AI Integration (Week 1)** #### 1.1 **AI Engine Service Enhancement** **File**: `backend/services/content_gap_analyzer/ai_engine_service.py` **Current Issues**: - All methods use hardcoded responses - No actual AI calls implemented - Missing integration with Gemini provider **Implementation Plan**: ```python # Add imports from backend.llm_providers.main_text_generation import llm_text_gen from backend.llm_providers.gemini_provider import gemini_structured_json_response # Replace hardcoded responses with AI calls async def analyze_content_gaps(self, analysis_summary: Dict[str, Any]) -> Dict[str, Any]: """Analyze content gaps using AI insights.""" try: prompt = f""" As an expert SEO content strategist, analyze this comprehensive content gap analysis data and provide actionable insights: TARGET ANALYSIS: - Website: {analysis_summary.get('target_url', 'N/A')} - Industry: {analysis_summary.get('industry', 'N/A')} - SERP Opportunities: {analysis_summary.get('serp_opportunities', 0)} keywords not ranking - Keyword Expansion: {analysis_summary.get('expanded_keywords_count', 0)} additional keywords identified - Competitors Analyzed: {analysis_summary.get('competitors_analyzed', 0)} websites DOMINANT CONTENT THEMES: {json.dumps(analysis_summary.get('dominant_themes', {}), indent=2)} PROVIDE: 1. Strategic Content Gap Analysis 2. Priority Content Recommendations (top 5) 3. Keyword Strategy Insights 4. Competitive Positioning Advice 5. Content Format Recommendations 6. Technical SEO Opportunities 7. Implementation Timeline (30/60/90 days) Format as JSON with clear, actionable recommendations. """ # Use structured JSON response for better parsing response = gemini_structured_json_response( prompt=prompt, schema={ "type": "object", "properties": { "strategic_insights": { "type": "array", "items": { "type": "object", "properties": { "type": {"type": "string"}, "insight": {"type": "string"}, "confidence": {"type": "number"}, "priority": {"type": "string"}, "estimated_impact": {"type": "string"} } } }, "content_recommendations": { "type": "array", "items": { "type": "object", "properties": { "type": {"type": "string"}, "recommendation": {"type": "string"}, "priority": {"type": "string"}, "estimated_traffic": {"type": "string"}, "implementation_time": {"type": "string"} } } }, "performance_predictions": { "type": "object", "properties": { "estimated_traffic_increase": {"type": "string"}, "estimated_ranking_improvement": {"type": "string"}, "estimated_engagement_increase": {"type": "string"}, "estimated_conversion_increase": {"type": "string"}, "confidence_level": {"type": "string"} } } } } ) return json.loads(response) except Exception as e: logger.error(f"Error in AI content gap analysis: {str(e)}") return {} ``` #### 1.2 **Keyword Researcher AI Integration** **File**: `backend/services/content_gap_analyzer/keyword_researcher.py` **Implementation Plan**: ```python # Add AI integration for keyword analysis async def _analyze_keyword_trends(self, industry: str, target_keywords: Optional[List[str]] = None) -> Dict[str, Any]: """Analyze keyword trends using AI.""" try: prompt = f""" Analyze keyword opportunities for {industry} industry: Target Keywords: {target_keywords or []} Provide comprehensive keyword analysis including: 1. Search volume estimates 2. Competition levels 3. Trend analysis 4. Opportunity scoring 5. Content format recommendations Format as structured JSON with detailed analysis. """ response = gemini_structured_json_response( prompt=prompt, schema={ "type": "object", "properties": { "trends": { "type": "object", "additionalProperties": { "type": "object", "properties": { "search_volume": {"type": "number"}, "difficulty": {"type": "number"}, "trend": {"type": "string"}, "competition": {"type": "string"}, "intent": {"type": "string"}, "cpc": {"type": "number"} } } }, "summary": { "type": "object", "properties": { "total_keywords": {"type": "number"}, "high_volume_keywords": {"type": "number"}, "low_competition_keywords": {"type": "number"}, "trending_keywords": {"type": "number"} } } } } ) return json.loads(response) except Exception as e: logger.error(f"Error analyzing keyword trends: {str(e)}") return {} ``` #### 1.3 **Competitor Analyzer AI Integration** **File**: `backend/services/content_gap_analyzer/competitor_analyzer.py` **Implementation Plan**: ```python # Add AI integration for competitor analysis async def _evaluate_market_position(self, competitors: List[Dict[str, Any]], industry: str) -> Dict[str, Any]: """Evaluate market position using AI.""" try: prompt = f""" Analyze the market position of competitors in the {industry} industry: Competitor Analyses: {json.dumps(competitors, indent=2)} Provide: 1. Market position analysis 2. Content gaps 3. Competitive advantages 4. Strategic positioning recommendations Format as structured JSON with detailed analysis. """ response = gemini_structured_json_response( prompt=prompt, schema={ "type": "object", "properties": { "market_leader": {"type": "string"}, "content_leader": {"type": "string"}, "quality_leader": {"type": "string"}, "market_gaps": { "type": "array", "items": {"type": "string"} }, "opportunities": { "type": "array", "items": {"type": "string"} }, "competitive_advantages": { "type": "array", "items": {"type": "string"} }, "strategic_recommendations": { "type": "array", "items": { "type": "object", "properties": { "type": {"type": "string"}, "recommendation": {"type": "string"}, "priority": {"type": "string"}, "estimated_impact": {"type": "string"} } } } } } ) return json.loads(response) except Exception as e: logger.error(f"Error evaluating market position: {str(e)}") return {} ``` ### **Phase 2: Advanced AI Features (Week 2)** #### 2.1 **Content Performance Prediction** ```python async def predict_content_performance(self, content_data: Dict[str, Any]) -> Dict[str, Any]: """Predict content performance using AI.""" try: prompt = f""" Predict content performance based on the following data: Content Data: {json.dumps(content_data, indent=2)} Provide detailed performance predictions including: 1. Traffic predictions 2. Engagement predictions 3. Ranking predictions 4. Conversion predictions 5. Risk factors 6. Success factors Format as structured JSON with confidence levels. """ response = gemini_structured_json_response( prompt=prompt, schema={ "type": "object", "properties": { "traffic_predictions": { "type": "object", "properties": { "estimated_monthly_traffic": {"type": "string"}, "traffic_growth_rate": {"type": "string"}, "peak_traffic_month": {"type": "string"}, "confidence_level": {"type": "string"} } }, "engagement_predictions": { "type": "object", "properties": { "estimated_time_on_page": {"type": "string"}, "estimated_bounce_rate": {"type": "string"}, "estimated_social_shares": {"type": "string"}, "estimated_comments": {"type": "string"}, "confidence_level": {"type": "string"} } }, "ranking_predictions": { "type": "object", "properties": { "estimated_ranking_position": {"type": "string"}, "estimated_ranking_time": {"type": "string"}, "ranking_confidence": {"type": "string"}, "competition_level": {"type": "string"} } }, "conversion_predictions": { "type": "object", "properties": { "estimated_conversion_rate": {"type": "string"}, "estimated_lead_generation": {"type": "string"}, "estimated_revenue_impact": {"type": "string"}, "confidence_level": {"type": "string"} } }, "risk_factors": { "type": "array", "items": {"type": "string"} }, "success_factors": { "type": "array", "items": {"type": "string"} } } } ) return json.loads(response) except Exception as e: logger.error(f"Error in AI performance prediction: {str(e)}") return {} ``` #### 2.2 **Strategic Intelligence Generation** ```python async def generate_strategic_insights(self, analysis_data: Dict[str, Any]) -> List[Dict[str, Any]]: """Generate strategic insights using AI.""" try: prompt = f""" Generate strategic insights based on the following analysis data: Analysis Data: {json.dumps(analysis_data, indent=2)} Provide strategic insights covering: 1. Content strategy recommendations 2. Competitive positioning advice 3. Content optimization suggestions 4. Innovation opportunities 5. Risk mitigation strategies Format as structured JSON with detailed insights. """ response = gemini_structured_json_response( prompt=prompt, schema={ "type": "object", "properties": { "strategic_insights": { "type": "array", "items": { "type": "object", "properties": { "type": {"type": "string"}, "insight": {"type": "string"}, "reasoning": {"type": "string"}, "priority": {"type": "string"}, "estimated_impact": {"type": "string"}, "implementation_time": {"type": "string"} } } } } } ) result = json.loads(response) return result.get('strategic_insights', []) except Exception as e: logger.error(f"Error generating AI strategic insights: {str(e)}") return [] ``` ### **Phase 3: AI Prompt Optimization (Week 3)** #### 3.1 **Enhanced AI Prompts** Based on the deep dive analysis, implement these advanced prompts: **Content Gap Analysis Prompt**: ```python CONTENT_GAP_ANALYSIS_PROMPT = """ As an expert SEO content strategist, analyze this comprehensive content gap analysis data and provide actionable insights: TARGET ANALYSIS: - Website: {target_url} - Industry: {industry} - SERP Opportunities: {serp_opportunities} keywords not ranking - Keyword Expansion: {expanded_keywords_count} additional keywords identified - Competitors Analyzed: {competitors_analyzed} websites DOMINANT CONTENT THEMES: {dominant_themes} PROVIDE: 1. Strategic Content Gap Analysis 2. Priority Content Recommendations (top 5) 3. Keyword Strategy Insights 4. Competitive Positioning Advice 5. Content Format Recommendations 6. Technical SEO Opportunities 7. Implementation Timeline (30/60/90 days) Format as JSON with clear, actionable recommendations. """ ``` **Market Position Analysis Prompt**: ```python MARKET_POSITION_PROMPT = """ Analyze the market position of competitors in the {industry} industry: Competitor Analyses: {competitor_analyses} Provide: 1. Market position analysis 2. Content gaps 3. Competitive advantages 4. Strategic positioning recommendations Format as JSON with detailed analysis. """ ``` **Keyword Analysis Prompt**: ```python KEYWORD_ANALYSIS_PROMPT = """ Analyze keyword opportunities for {industry} industry: Keyword Trends: {trend_analysis} Search Intent: {intent_analysis} Opportunities: {opportunities} Provide: 1. High-priority keyword recommendations 2. Content format suggestions 3. Topic cluster development 4. Search intent optimization Format as JSON with detailed analysis. """ ``` ### **Phase 4: AI Service Integration (Week 4)** #### 4.1 **Create AI Service Manager** **File**: `backend/services/ai_service_manager.py` ```python """ AI Service Manager Centralized AI service management for content planning system. """ from typing import Dict, Any, List, Optional from loguru import logger import json from backend.llm_providers.main_text_generation import llm_text_gen from backend.llm_providers.gemini_provider import gemini_structured_json_response class AIServiceManager: """Manages AI service interactions and prompt handling.""" def __init__(self): """Initialize AI service manager.""" self.logger = logger self.prompts = self._load_prompts() def _load_prompts(self) -> Dict[str, str]: """Load AI prompts from configuration.""" return { 'content_gap_analysis': CONTENT_GAP_ANALYSIS_PROMPT, 'market_position': MARKET_POSITION_PROMPT, 'keyword_analysis': KEYWORD_ANALYSIS_PROMPT, 'performance_prediction': PERFORMANCE_PREDICTION_PROMPT, 'strategic_insights': STRATEGIC_INSIGHTS_PROMPT } async def generate_content_gap_analysis(self, analysis_data: Dict[str, Any]) -> Dict[str, Any]: """Generate content gap analysis using AI.""" try: prompt = self.prompts['content_gap_analysis'].format(**analysis_data) response = gemini_structured_json_response( prompt=prompt, schema=CONTENT_GAP_ANALYSIS_SCHEMA ) return json.loads(response) except Exception as e: self.logger.error(f"Error generating content gap analysis: {str(e)}") return {} async def generate_market_position_analysis(self, market_data: Dict[str, Any]) -> Dict[str, Any]: """Generate market position analysis using AI.""" try: prompt = self.prompts['market_position'].format(**market_data) response = gemini_structured_json_response( prompt=prompt, schema=MARKET_POSITION_SCHEMA ) return json.loads(response) except Exception as e: self.logger.error(f"Error generating market position analysis: {str(e)}") return {} async def generate_keyword_analysis(self, keyword_data: Dict[str, Any]) -> Dict[str, Any]: """Generate keyword analysis using AI.""" try: prompt = self.prompts['keyword_analysis'].format(**keyword_data) response = gemini_structured_json_response( prompt=prompt, schema=KEYWORD_ANALYSIS_SCHEMA ) return json.loads(response) except Exception as e: self.logger.error(f"Error generating keyword analysis: {str(e)}") return {} ``` #### 4.2 **Update All Services to Use AI Manager** ```python # In each service file, replace hardcoded responses with AI calls from services.ai_service_manager import AIServiceManager class AIEngineService: def __init__(self): self.ai_manager = AIServiceManager() logger.info("AIEngineService initialized") async def analyze_content_gaps(self, analysis_summary: Dict[str, Any]) -> Dict[str, Any]: """Analyze content gaps using AI insights.""" return await self.ai_manager.generate_content_gap_analysis(analysis_summary) async def analyze_market_position(self, market_data: Dict[str, Any]) -> Dict[str, Any]: """Analyze market position using AI insights.""" return await self.ai_manager.generate_market_position_analysis(market_data) ``` ## 📊 **Implementation Timeline** ### **Week 1: Core AI Integration** ✅ **COMPLETED** - [x] Replace hardcoded responses in AI Engine Service - [x] Integrate Gemini provider calls - [x] Implement basic AI prompts - [x] Test AI functionality ### **Week 2: Advanced AI Features** ✅ **COMPLETED** - [x] Implement content performance prediction - [x] Add strategic intelligence generation - [x] Create comprehensive AI schemas - [x] Optimize AI prompts ### **Week 3: AI Prompt Optimization** ✅ **COMPLETED** - [x] Implement advanced prompts from deep dive - [x] Create structured JSON schemas - [x] Optimize prompt performance - [x] Add error handling and fallbacks **Status Update**: ✅ **AI Prompt Optimizer Service fully implemented** - Advanced AI prompts from deep dive analysis implemented - Comprehensive JSON schemas for structured responses - Optimized prompt performance with expert-level instructions - Robust error handling and fallback mechanisms - Integration with existing AI engine service ### **Week 4: AI Service Integration** ✅ **COMPLETED** - [x] Create AI Service Manager - [x] Update all services to use AI Manager - [x] Implement centralized AI configuration - [x] Add AI performance monitoring **Status Update**: ✅ **AI Service Manager fully implemented** - Centralized AI service management with performance monitoring - All services updated to use AI Service Manager - Centralized AI configuration with timeout and retry settings - Comprehensive AI performance monitoring with metrics tracking - Service breakdown by AI type with success rates and response times ## ✅ **Phase 4 Status Update** ### **Completed Tasks** 1. **✅ AI Service Manager** - Centralized AI service management with performance monitoring - Comprehensive AI configuration with timeout and retry settings - Service breakdown by AI type with success rates and response times - Performance metrics tracking and health monitoring - Centralized prompt and schema management 2. **✅ Service Integration** - AI Engine Service updated to use AI Service Manager - All AI calls routed through centralized manager - Performance monitoring and metrics collection - Error handling and fallback mechanisms - Health check integration 3. **✅ Performance Monitoring** - AI call performance metrics tracking - Service breakdown by AI type - Success rate monitoring - Response time tracking - Error rate monitoring ### **New Features Implemented** - **Centralized AI Management**: Single point of control for all AI services - **Performance Monitoring**: Real-time metrics for AI service performance - **Service Breakdown**: Detailed metrics by AI service type - **Configuration Management**: Centralized AI configuration settings - **Health Monitoring**: Comprehensive health checks for AI services ### **Quality Criteria** - [ ] AI response accuracy > 85% - [ ] AI response time < 10 seconds - [ ] AI error rate < 5% - [ ] AI fallback mechanisms working - [ ] AI prompts optimized for quality ## 🔧 **Implementation Steps** ### **Step 1: Environment Setup** 1. Verify Gemini API key configuration 2. Test Gemini provider functionality 3. Set up AI service monitoring 4. Configure error handling ### **Step 2: Core Integration** 1. Update AI Engine Service with real AI calls 2. Implement structured JSON responses 3. Add comprehensive error handling 4. Test AI functionality ### **Step 3: Service Updates** 1. Update Keyword Researcher with AI integration 2. Update Competitor Analyzer with AI integration 3. Update Website Analyzer with AI integration 4. Test all services with AI ### **Step 4: Optimization** 1. Optimize AI prompts for better results 2. Implement AI response caching 3. Add AI performance monitoring 4. Create AI fallback mechanisms ## 📈 **Expected Outcomes** ### **Immediate Benefits** - ✅ Real AI-powered insights instead of hardcoded data - ✅ Dynamic content recommendations - ✅ Intelligent keyword analysis - ✅ Strategic competitive intelligence ### **Long-term Benefits** - ✅ Improved content strategy accuracy - ✅ Better keyword targeting - ✅ Enhanced competitive positioning - ✅ Optimized content performance --- **Status**: Ready for Implementation **Priority**: High **Estimated Duration**: 4 weeks **Dependencies**: Gemini API key, existing AI infrastructure