Content Optimization For Query Fan-Out
Runs an existing page through a four-stage query fan-out analysis to find every related question it should be answering but isn't. - Inputs: existing content, primary topic/keyword, target audience, content goal - Extracts entities and themes, then maps likely user queries per theme - Scores each query as fully, partially, or not addressed, with a 1-10 depth rating - Splits gaps into critical, opportunity, and enhancement tiers - Rebuilds the piece as a 20-25 heading outline flagging new gap-filling sections
Prompt
# Content Optimization for Query Fan-Out ## Input Requirements 1. **Existing Content**: {existing_content} 2. **Primary Topic/Keyword**: {primary_topic} 3. **Target Audience**: {target_audience} 4. **Content Goal**: {content_goal} ## Automated Query Fan-Out Analysis Process ### Stage 1: Content Decomposition & Entity Extraction From the content you provide, the system will: - **Extract Primary Entities**: Identify the main concepts, products, services, or topics covered - **Identify Attributes**: Locate descriptive elements (quality, size, time, cost) - **Map Relationships**: Uncover how concepts connect within your content - **Detect Intent Signals**: Recognize which actions or outcomes your content encourages ### Stage 2: Generate Query Fan-Out from Content Drawing on your content's entities and themes, generate the likely query landscape: #### 2.1 Theme Identification Pull 3-5 core themes from your content: - Each theme = a distinct topic cluster within the content - Themes drawn from your headings, recurring concepts, and semantic groupings #### 2.2 Hierarchical Query Generation For each identified theme, create: - **Level 1**: Broad queries about the theme - **Level 2**: Specific aspects users would ask - **Level 3**: Detailed implementation questions - **Level 4**: Contextual variations for different users #### 2.3 Query Patterns to Generate - **Direct Questions**: What your content explicitly answers - **Implied Questions**: What users would ask next after reading - **Gap Questions**: Related queries your content ought to address - **Bridge Questions**: Connections between your themes ### Stage 3: Coverage Analysis #### 3.1 Query-Content Matching For each generated query, judge: - **Fully Addressed**: Query has dedicated content - **Partially Addressed**: Query mentioned but not detailed - **Not Addressed**: Query absent from content #### 3.2 Semantic Depth Scoring Evaluate how thoroughly each theme is handled: - **Surface level** (definitions only): 1-3 - **Moderate depth** (explanations): 4-6 - **Comprehensive** (actionable details): 7-9 - **Expert level** (nuanced insights): 10 ### Stage 4: Gap Identification & Prioritization #### 4.1 Critical Gaps (Must Address) - Centroid queries of main themes left uncovered - Basic "what is" questions unanswered - Primary entity relationships missing - User intent misalignment #### 4.2 Opportunity Gaps (Should Address) - Comparison queries not covered - How-to/implementation details missing - Alternative solutions not discussed - Common problems/objections unaddressed #### 4.3 Enhancement Gaps (Could Address) - Advanced use cases - Edge cases and exceptions - Industry-specific variations - Future trends/predictions ## OUTPUT FORMATTING REQUIREMENTS **CRITICAL: Produce an optimized content outline as an article headings structure that covers all identified query gaps:** ### Content Context The output should be formatted as an **improved article outline (H2/H3 tags)** that thoroughly covers both existing content and identified gaps. This yields an SEO-optimized content structure targeting all relevant fan-out queries. ### Output Structure Present the optimized content outline as an article heading hierarchy:
