ALwrity persona system
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ajaysi
319
backend/services/persona/linkedin/linkedin_persona_prompts.py
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backend/services/persona/linkedin/linkedin_persona_prompts.py
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"""
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LinkedIn Persona Prompts
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Contains LinkedIn-specific prompt generation for persona analysis.
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"""
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from typing import Dict, Any
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import json
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from loguru import logger
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class LinkedInPersonaPrompts:
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"""Handles LinkedIn-specific persona prompt generation."""
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@staticmethod
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def build_enhanced_linkedin_prompt(core_persona: Dict[str, Any], onboarding_data: Dict[str, Any]) -> str:
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"""Build enhanced LinkedIn-specific persona prompt with professional optimization."""
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# Extract comprehensive professional context
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professional_context = LinkedInPersonaPrompts._extract_professional_context(onboarding_data)
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website_analysis = onboarding_data.get("website_analysis", {}) or {}
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target_audience = website_analysis.get("target_audience", {})
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industry_focus = professional_context.get("industry_focus", "general")
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expertise_level = professional_context.get("expertise_level", "intermediate")
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prompt = f"""
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LINKEDIN PROFESSIONAL PERSONA OPTIMIZATION TASK: Create a comprehensive LinkedIn-optimized writing persona for professional networking and thought leadership.
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CORE PERSONA FOUNDATION:
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{json.dumps(core_persona, indent=2)}
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PROFESSIONAL CONTEXT:
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- Industry: {industry_focus}
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- Expertise Level: {expertise_level}
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- Company Size: {professional_context.get('company_size', 'Not specified')}
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- Business Model: {professional_context.get('business_model', 'Not specified')}
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- Professional Role: {professional_context.get('professional_role', 'Not specified')}
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- Demographics: {professional_context.get('target_demographics', [])}
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- Psychographic: {professional_context.get('psychographic_profile', 'Not specified')}
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LINKEDIN PLATFORM SPECIFICATIONS:
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- Character Limit: 3,000 characters
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- Optimal Post Length: 150-300 words for maximum engagement
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- Professional Network: B2B focused, career-oriented audience
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- Algorithm Priority: Engagement, relevance, professional value
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- Content Types: Posts, Articles, Polls, Videos, Carousels, Events
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- Hashtag Limit: 3-5 hashtags for optimal reach
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- Link Strategy: Place external links in first comment for algorithm optimization
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LINKEDIN PROFESSIONAL OPTIMIZATION REQUIREMENTS:
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1. PROFESSIONAL TONE & VOICE:
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- Maintain authoritative yet approachable professional tone
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- Use industry-specific terminology appropriately
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- Balance expertise with accessibility for {expertise_level} audience
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- Incorporate thought leadership elements
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- Include professional storytelling and case studies
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2. CONTENT STRATEGY FOR {industry_focus.upper()}:
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- Industry insights for {expertise_level} professionals
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- Professional development content for {professional_context.get('target_demographics', [])}
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- Business strategy discussions for {professional_context.get('business_model', 'general business')}
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- Networking focus for {professional_context.get('company_size', 'all company sizes')}
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- Thought leadership positioning as {professional_context.get('professional_role', 'professional')}
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3. ENGAGEMENT OPTIMIZATION:
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- Professional question frameworks for discussion
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- Industry-relevant polling strategies
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- Professional networking call-to-actions
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- Thought leadership positioning
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- Community building through professional value
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4. LINKEDIN-SPECIFIC FEATURES:
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- Native video optimization for professional content
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- LinkedIn Articles for long-form thought leadership
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- LinkedIn Polls for industry insights and engagement
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- LinkedIn Events for professional networking
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- LinkedIn Carousels for educational content
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- LinkedIn Live for professional discussions
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5. PROFESSIONAL NETWORKING ELEMENTS:
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- Industry-specific hashtag strategy
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- Professional mention and tagging etiquette
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- Thought leadership positioning
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- Professional relationship building
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- Career advancement focus
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6. CONTENT FORMAT OPTIMIZATION:
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- Hook strategies for professional feed
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- "See More" optimization for longer posts
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- Professional call-to-action frameworks
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- Industry-specific content structures
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- Professional storytelling techniques
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7. LINKEDIN ALGORITHM OPTIMIZATION:
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- Professional engagement patterns
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- Industry-relevant content timing
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- Professional network interaction strategies
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- Thought leadership content performance
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- Professional community building
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8. INDUSTRY-SPECIFIC ADAPTATIONS FOR {industry_focus.upper()}:
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- Terminology appropriate for {expertise_level} level
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- Professional development for {professional_context.get('target_demographics', [])}
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- Trend discussions for {professional_context.get('business_model', 'general business')}
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- Networking strategies for {professional_context.get('company_size', 'all company sizes')}
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- Thought leadership as {professional_context.get('professional_role', 'professional')}
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- Content addressing {professional_context.get('psychographic_profile', 'professional needs')}
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- Business insights for {professional_context.get('conversion_focus', 'business growth')}
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PROFESSIONAL EXCELLENCE STANDARDS:
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- Maintain high professional standards
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- Focus on value-driven content
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- Emphasize thought leadership and expertise
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- Build professional credibility and authority
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- Foster meaningful professional relationships
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- Provide actionable business insights
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- Support professional development and growth
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Generate a comprehensive LinkedIn-optimized persona that positions the user as a thought leader in {industry_focus} while maintaining professional excellence and maximizing LinkedIn's professional networking potential.
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"""
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return prompt
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@staticmethod
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def get_linkedin_platform_constraints() -> Dict[str, Any]:
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"""Get LinkedIn-specific platform constraints and best practices."""
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return {
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"character_limit": 3000,
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"optimal_length": "150-300 words",
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"professional_tone": True,
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"hashtag_limit": 5,
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"rich_media": True,
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"long_form": True,
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"thought_leadership": True,
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"networking_focus": True,
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"career_development": True,
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"industry_insights": True,
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"professional_storytelling": True,
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"b2b_optimized": True,
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"algorithm_engagement": True,
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"native_video": True,
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"linkedin_articles": True,
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"linkedin_polls": True,
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"linkedin_events": True,
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"linkedin_carousels": True,
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"linkedin_live": True
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}
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@staticmethod
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def _extract_professional_context(onboarding_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Extract comprehensive professional context from onboarding data."""
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professional_context = {
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"industry_focus": "general",
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"expertise_level": "intermediate",
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"company_size": "Not specified",
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"business_model": "Not specified",
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"professional_role": "Not specified",
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"geographic_focus": "global",
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"target_demographics": [],
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"psychographic_profile": "",
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"content_purpose": "",
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"conversion_focus": "",
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"research_depth": "",
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"content_types": []
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}
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# Extract from website analysis
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website_analysis = onboarding_data.get("website_analysis", {}) or {}
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# Target audience information
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target_audience = website_analysis.get("target_audience", {})
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if target_audience:
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professional_context["industry_focus"] = target_audience.get("industry_focus", "general")
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professional_context["expertise_level"] = target_audience.get("expertise_level", "intermediate")
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professional_context["geographic_focus"] = target_audience.get("geographic_focus", "global")
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professional_context["target_demographics"] = target_audience.get("demographics", [])
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professional_context["psychographic_profile"] = target_audience.get("psychographic_profile", "")
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# Content type and business context
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content_type = website_analysis.get("content_type", {})
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if content_type:
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professional_context["content_purpose"] = content_type.get("purpose", "")
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professional_context["conversion_focus"] = content_type.get("conversion_focus", "")
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# Company and business information from crawl results
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crawl_result = website_analysis.get("crawl_result", {})
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if crawl_result:
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domain_info = crawl_result.get("domain_info", {})
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if domain_info:
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professional_context["company_size"] = domain_info.get("company_size", "Not specified")
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professional_context["business_model"] = domain_info.get("business_model", "Not specified")
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brand_info = crawl_result.get("brand_info", {})
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if brand_info:
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professional_context["professional_role"] = brand_info.get("professional_role", "Not specified")
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# Research preferences
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research_prefs = onboarding_data.get("research_preferences", {})
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if research_prefs:
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professional_context["research_depth"] = research_prefs.get("research_depth", "")
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professional_context["content_types"] = research_prefs.get("content_types", [])
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# Enhanced analysis data
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enhanced_analysis = onboarding_data.get("enhanced_analysis", {})
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if enhanced_analysis:
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audience_intel = enhanced_analysis.get("audience_intelligence", {})
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if audience_intel:
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# Override with more detailed information if available
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if audience_intel.get("industry_focus"):
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professional_context["industry_focus"] = audience_intel["industry_focus"]
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if audience_intel.get("expertise_level"):
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professional_context["expertise_level"] = audience_intel["expertise_level"]
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if audience_intel.get("psychographic_profile"):
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professional_context["psychographic_profile"] = audience_intel["psychographic_profile"]
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brand_voice = enhanced_analysis.get("brand_voice_analysis", {})
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if brand_voice:
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if brand_voice.get("primary_content_type"):
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professional_context["content_purpose"] = brand_voice["primary_content_type"]
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if brand_voice.get("conversion_focus"):
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professional_context["conversion_focus"] = brand_voice["conversion_focus"]
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return professional_context
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@staticmethod
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def build_linkedin_system_prompt(core_persona: Dict[str, Any]) -> str:
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"""
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Build system prompt with core persona for LinkedIn generation.
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This moves the core persona to system prompt to free up context window.
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"""
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import json
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return f"""You are an expert LinkedIn content strategist and professional networking specialist.
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CORE PERSONA FOUNDATION:
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{json.dumps(core_persona, indent=2)}
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Your task is to create LinkedIn-optimized persona adaptations that maintain the core persona's identity while optimizing for professional networking, thought leadership, and B2B engagement on LinkedIn.
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Focus on:
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- Professional tone and authority
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- Industry-specific optimization
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- LinkedIn algorithm best practices
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- B2B engagement strategies
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- Professional networking optimization"""
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@staticmethod
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def build_focused_linkedin_prompt(onboarding_data: Dict[str, Any]) -> str:
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"""
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Build focused LinkedIn prompt without core persona JSON to optimize context usage.
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"""
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# Extract professional context
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professional_context = LinkedInPersonaPrompts._extract_professional_context(onboarding_data)
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industry_focus = professional_context.get("industry_focus", "general")
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expertise_level = professional_context.get("expertise_level", "intermediate")
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return f"""LINKEDIN PROFESSIONAL OPTIMIZATION TASK: Create LinkedIn-specific adaptations for the core persona.
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PROFESSIONAL CONTEXT:
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- Industry: {industry_focus}
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- Expertise Level: {expertise_level}
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- Company Size: {professional_context.get('company_size', 'Not specified')}
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- Business Model: {professional_context.get('business_model', 'Not specified')}
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- Professional Role: {professional_context.get('professional_role', 'Not specified')}
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- Demographics: {professional_context.get('target_demographics', [])}
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- Psychographic: {professional_context.get('psychographic_profile', 'Not specified')}
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LINKEDIN PLATFORM SPECIFICATIONS:
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- Character Limit: 3,000 characters
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- Optimal Post Length: 150-300 words for maximum engagement
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- Professional Network: B2B focused, career-oriented audience
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- Algorithm Priority: Engagement, relevance, professional value
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- Content Types: Posts, Articles, Polls, Videos, Carousels, Events
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- Hashtag Limit: 3-5 hashtags for optimal reach
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- Link Strategy: Place external links in first comment for algorithm optimization
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LINKEDIN OPTIMIZATION REQUIREMENTS:
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1. PROFESSIONAL TONE & VOICE:
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- Maintain authoritative yet approachable professional tone
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- Use industry-specific terminology appropriately
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- Balance expertise with accessibility for {expertise_level} audience
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- Incorporate thought leadership elements
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- Include professional storytelling and case studies
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2. CONTENT STRATEGY FOR {industry_focus.upper()}:
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- Industry insights for {expertise_level} professionals
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- Professional development content for {professional_context.get('target_demographics', [])}
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- Business strategy discussions for {professional_context.get('business_model', 'general business')}
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- Networking focus for {professional_context.get('company_size', 'all company sizes')}
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- Thought leadership positioning as {professional_context.get('professional_role', 'professional')}
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3. LINKEDIN-SPECIFIC ADAPTATIONS:
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- Optimize sentence structure for professional readability
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- Create platform-specific vocabulary and terminology
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- Define engagement patterns for B2B audience
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- Establish professional networking strategies
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- Include LinkedIn feature optimization (Articles, Polls, Events, etc.)
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4. ALGORITHM OPTIMIZATION:
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- Engagement patterns for professional audience
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- Content timing for maximum reach
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- Professional value metrics
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- Network interaction strategies
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5. PROFESSIONAL CONTEXT OPTIMIZATION:
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- Industry-specific positioning
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- Expertise level adaptation
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- Company size considerations
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- Business model alignment
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- Professional role authority
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- Demographic targeting
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- Psychographic engagement
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- Conversion optimization
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Generate a comprehensive LinkedIn-optimized persona that maintains the core persona's identity while maximizing professional networking and thought leadership potential on LinkedIn."""
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