moreminimore-marketing/docs/ALwrity Researcher/PHASE1_IMPLEMENTATION_SUMMARY.md

Phase 1 Implementation Summary: Research Persona Enhancements

Date: 2025-12-31

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✅ Phase 1 Implementation Complete

What Was Implemented:

1. Content Type → Preset Generation ✅

Enhancement: Generate presets based on actual content types from website analysis

Changes Made:

• Extract content_type from website analysis (primary_type, secondary_types, purpose)
• Added instructions to generate content-type-specific presets:
  • Blog → "Blog Topic Research" preset
  • Article → "Article Research" preset
  • Case Study → "Case Study Research" preset
  • Tutorial → "Tutorial Research" preset
  • Thought Leadership → "Thought Leadership Research" preset
  • Education → "Educational Content Research" preset
• Preset names now include content type when relevant
• Research mode selection considers content_type.purpose

Impact: Presets now match user's actual content creation needs

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2. Writing Style Complexity → Research Depth ✅

Enhancement: Map writing style complexity to research depth preferences

Changes Made:

• Extract writing_style.complexity from website analysis
• Added mapping logic:
  • complexity == "high" → default_research_mode = "comprehensive"
  • complexity == "medium" → default_research_mode = "targeted"
  • complexity == "low" → default_research_mode = "basic"
• Fallback to research_preferences.research_depth if complexity not available

Impact: Research depth now matches user's writing sophistication level

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3. Crawl Result Topics → Suggested Keywords ✅

Enhancement: Extract topics and keywords from actual website content

Changes Made:

• Added _extract_topics_from_crawl() method:
  • Extracts from topics, headings, titles, sections, metadata
  • Returns top 15 unique topics
• Added _extract_keywords_from_crawl() method:
  • Extracts from keywords, metadata, tags, content frequency
  • Returns top 20 unique keywords
• Updated prompt to prioritize extracted keywords:
  • First use extracted_keywords (top 8-10)
  • Then supplement with industry/interests keywords
  • Total: 8-12 keywords, with 50%+ from extracted_keywords

Impact: Keywords now reflect user's actual website content topics

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📋 Code Changes

File Modified: backend/services/research/research_persona_prompt_builder.py

Added:

1. Extraction of writing_style, content_type, crawl_result from website analysis
2. _extract_topics_from_crawl() method
3. _extract_keywords_from_crawl() method
4. Enhanced prompt instructions for:
   • Content-type-based preset generation
   • Complexity-based research depth mapping
   • Extracted keywords prioritization

Prompt Enhancements:

• Added "PHASE 1: WEBSITE ANALYSIS INTELLIGENCE" section
• Enhanced "DEFAULT VALUES" section with complexity mapping
• Enhanced "KEYWORD INTELLIGENCE" section with extracted keywords priority
• Enhanced "RECOMMENDED PRESETS" section with content-type-specific generation

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🎯 Expected Benefits

1. More Accurate Presets: Based on actual content types (blog, tutorial, case study, etc.)
2. Aligned Research Depth: Matches writing complexity (high complexity → comprehensive research)
3. Relevant Keywords: Uses actual website topics instead of generic industry keywords
4. Better Personalization: Research persona reflects user's actual content strategy

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🧪 Testing Recommendations

1. Test with Different Content Types:

   • User with blog content → Should see "Blog Topic Research" preset
   • User with tutorial content → Should see "Tutorial Research" preset
   • User with case study content → Should see "Case Study Research" preset

2. Test Complexity Mapping:

   • High complexity writing → Should get "comprehensive" research mode
   • Low complexity writing → Should get "basic" research mode

3. Test Keyword Extraction:

   • User with crawl_result → Should see extracted keywords in suggested_keywords
   • User without crawl_result → Should fall back to industry keywords

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📝 Next Steps (Phase 2 & 3)

Phase 2: Medium Impact, Medium Effort

• Extract style_patterns → Generate pattern-based research angles
• Extract content_characteristics.vocabulary → Sophisticated keyword expansion
• Extract style_guidelines → Query enhancement rules

Phase 3: High Impact, High Effort

• Full crawl_result analysis → Topic extraction, theme identification
• Complete writing style mapping → All research preferences
• Content strategy intelligence → Comprehensive preset generation

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✅ Implementation Status

• ✅ Content type extraction and preset generation
• ✅ Writing style complexity mapping to research depth
• ✅ Crawl result topic/keyword extraction
• ✅ Enhanced prompt instructions
• ✅ Helper methods for data extraction

Status: Phase 1 Complete - Ready for Testing