issues fixes related to autaitmioan and ai idea genration
This commit is contained in:
IGNY8 VPS (Salman)
@@ -205,7 +205,10 @@ class GenerateIdeasFunction(BaseAIFunction):
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elif not isinstance(description, str):
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description = str(description)
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# Handle target_keywords
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# Handle primary_focus_keywords (new field)
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primary_focus_keywords = idea_data.get('primary_focus_keywords', '')
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# Handle target_keywords (covered_keywords from AI)
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target_keywords = idea_data.get('covered_keywords', '') or idea_data.get('target_keywords', '')
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# Direct mapping - no conversion needed
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@@ -216,6 +219,7 @@ class GenerateIdeasFunction(BaseAIFunction):
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ContentIdeas.objects.create(
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idea_title=idea_data.get('title', 'Untitled Idea'),
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description=description, # Stored as JSON string
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primary_focus_keywords=primary_focus_keywords,
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content_type=content_type,
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content_structure=content_structure,
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target_keywords=target_keywords,
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@@ -82,14 +82,41 @@ def run_automation_task(self, run_id: str):
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try:
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service = AutomationService.from_run_id(run_id)
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# Run all stages sequentially
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# Run all stages sequentially, checking for pause/cancel between stages
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service.run_stage_1()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 1")
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return
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service.run_stage_2()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 2")
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return
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service.run_stage_3()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 3")
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return
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service.run_stage_4()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 4")
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return
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service.run_stage_5()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 5")
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return
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service.run_stage_6()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 6")
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return
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service.run_stage_7()
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if service.run.status in ['paused', 'cancelled']:
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logger.info(f"[AutomationTask] Automation {service.run.status} after stage 7")
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return
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logger.info(f"[AutomationTask] Completed automation run: {run_id}")
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@@ -244,7 +244,8 @@ class ContentIdeas(SoftDeletableModel, SiteSectorBaseModel):
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idea_title = models.CharField(max_length=255, db_index=True)
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description = models.TextField(blank=True, null=True)
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target_keywords = models.CharField(max_length=500, blank=True) # Comma-separated keywords (legacy)
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primary_focus_keywords = models.CharField(max_length=500, blank=True, help_text="1-2 main keywords this content targets")
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target_keywords = models.CharField(max_length=500, blank=True) # Comma-separated keywords (covered_keywords from AI)
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keyword_objects = models.ManyToManyField(
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'Keywords',
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blank=True,
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@@ -0,0 +1,18 @@
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# Generated migration for adding primary_focus_keywords field
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from django.db import migrations, models
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class Migration(migrations.Migration):
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dependencies = [
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('planner', '0010_add_taxonomy_soft_delete_and_keyword_fk_cascade'),
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]
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operations = [
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# Add primary_focus_keywords field to ContentIdeas
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migrations.AddField(
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model_name='contentideas',
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name='primary_focus_keywords',
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field=models.CharField(blank=True, help_text='1-2 main keywords this content targets', max_length=500),
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),
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]
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@@ -245,6 +245,7 @@ class ContentIdeasSerializer(serializers.ModelSerializer):
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'id',
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'idea_title',
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'description',
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'primary_focus_keywords',
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'content_type',
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'content_structure',
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'target_keywords',
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280
backend/scripts/update_ideas_prompt.sql
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280
backend/scripts/update_ideas_prompt.sql
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@@ -0,0 +1,280 @@
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-- Update the Ideas Generation prompt in GlobalAIPrompt
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-- Run this SQL to update the prompt
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UPDATE igny8_global_ai_prompts
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SET prompt_value = '# SEO Content Idea Generator
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You are a content strategist. Generate content ideas and simple outlines for keyword clusters. The actual content will be written by a separate system.
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---
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## INPUT FORMAT
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**Clusters to analyze:**
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[IGNY8_CLUSTERS]
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**Keywords in each cluster:**
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[IGNY8_CLUSTER_KEYWORDS]
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---
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## OUTPUT REQUIREMENTS
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Generate **3-7 content ideas per cluster** based on the number of unique major topics within the cluster:
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- Analyze the cluster keywords to identify distinct major topics
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- Generate one idea per major topic (not for minor keyword variations)
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- Each idea must target completely different major topics/angles
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**Important**:
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- Focus on **major topic differences**, not minor keyword variations
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- Each idea must cover **completely different keywords** from the cluster
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- Generate fewer ideas for focused clusters, more for broad clusters
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---
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## OUTPUT JSON OBJECT STRUCTURE
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{
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"ideas": [
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{
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"title": "[Compelling title with primary focus keyword]",
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"description": {
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"overview": "[2-3 sentence description of what this content covers and its unique angle]",
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"outline": {
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"intro_focus": "[What the introduction should establish]",
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"main_sections": [
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{
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"h2_topic": "[Section topic/angle - must include covered keywords]",
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"coverage": "[What this section should cover - 1 sentence]"
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}
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]
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}
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},
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"primary_focus_keywords": "[1-2 main keywords this content targets, comma-separated]",
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"covered_keywords": "[2-3 additional supporting keywords, comma-separated]",
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"content_type": "post|page",
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"content_structure": "guide_tutorial|how_to|comparison|review|top_listicle|question",
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"cluster_id": "[Cluster ID number]",
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"estimated_word_count": "[AI determined based on sections and keyword coverage]"
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}
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]
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}
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---
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## KEYWORD REQUIREMENTS
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### Primary Focus Keywords (1-2 per idea)
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The main keywords this content primarily targets:
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- **MUST appear in the title**
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- **MUST appear in at least 2 different H2 section headings**
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- These drive the content''s core topic
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- These have the highest search priority
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### Covered Keywords (2-3 per idea)
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Additional supporting keywords from the cluster:
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- Include related search terms from cluster
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- Include natural variations and long-tail keywords
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- Should also appear in H2 section headings where natural
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**Total Keywords Per Idea**: 3-5 maximum (1-2 primary + 2-3 covered)
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### Keyword Distribution Strategy
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- **Each of the ideas must cover completely different keywords**
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- **Focus on major topic differences, not minor variations**
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- Minimize overlap between ideas in the same cluster
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- Each idea should target a distinct subtopic within the cluster
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**Example for "Email Marketing" cluster:**
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**Idea 1**: "How to Build an Email List from Scratch"
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- primary_focus_keywords: "email list building"
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- covered_keywords: "subscriber acquisition, lead magnets"
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- Total: 3 keywords covering list growth subtopic
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**Idea 2**: "Best Email Marketing Platforms Compared"
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- primary_focus_keywords: "email marketing platforms"
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- covered_keywords: "email software, automation tools"
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- Total: 3 keywords covering platform selection subtopic
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**Idea 3**: "Email Campaign Strategy Guide"
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- primary_focus_keywords: "email campaigns"
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- covered_keywords: "campaign optimization, conversion tactics"
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- Total: 3 keywords covering campaign execution subtopic
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---
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## CONTENT STRUCTURE TYPES
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### Guide/Tutorial
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- **Purpose**: Step-by-step educational content
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- **Coverage**: Process-oriented, actionable steps
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- **Sections**: AI determines based on content depth and keywords
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### How-To
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- **Purpose**: Solve a specific problem
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- **Coverage**: Problem → solution framework
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- **Sections**: AI determines based on complexity
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### Comparison
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- **Purpose**: Compare options/alternatives
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- **Coverage**: Feature analysis, pros/cons, recommendations
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- **Sections**: AI determines based on items compared
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### Review
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- **Purpose**: Evaluate specific products/services
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- **Coverage**: Features, testing, verdict
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- **Sections**: AI determines based on review depth
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### Top Listicle
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- **Purpose**: Curated ranked list
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- **Coverage**: Criteria, ranked items, selection guide
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- **Sections**: AI determines based on list size
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### Question
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- **Purpose**: Answer specific query
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- **Coverage**: Question → context → answer → implications
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- **Sections**: AI determines based on answer complexity
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---
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## WORD COUNT DETERMINATION
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**AI must determine estimated_word_count based on:**
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- Number of H2 sections outlined
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- Number of keywords to be covered (3-5 total)
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- Content structure complexity
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- Topic depth requirements
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**Guideline**: More sections + more keywords = higher word count
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**Range**: Typically 1200-1800 words, but AI decides the optimal length
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---
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## OUTLINE REQUIREMENTS
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For each idea, provide:
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1. **Intro Focus**: What angle/hook the introduction should take (1 sentence)
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2. **Main Sections**: Variable number of H2 topics based on content needs
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- **CRITICAL**: Primary focus keywords must appear in at least 2 different H2 section headings
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- Each covered keyword should appear in H2 headings where natural
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- Integrate keywords naturally into section titles
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- 1 sentence on what each section should cover
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- AI determines optimal section count for topic coverage
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- No need for H3 breakdown (content generator will handle)
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- No formatting details (content generator will handle)
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3. **Section approach**:
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- Foundation/basics sections (if needed)
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- Core concept sections (main body)
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- Application/implementation sections
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- Advanced/future sections (if appropriate)
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- AI determines which sections are necessary
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---
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## SECTION COUNT DETERMINATION
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**AI must determine the number of H2 sections based on:**
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- Content structure type requirements
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- Number of keywords to cover (3-5 total)
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- Topic complexity and depth
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- Natural content flow
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**Do not use hardcoded section counts** - evaluate each idea independently.
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---
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## TITLE GUIDELINES
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- **MUST include primary focus keywords (1-2 keywords)**
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- 50-65 characters ideal
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- Compelling and specific
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- Match content structure type:
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- How-to: "How to [Action] [Object/Goal]"
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- Comparison: "[X] vs [Y]: Which Is Better?"
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- Review: "[Product/Service] Review: [Key Benefit]"
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- Listicle: "[Number] Best [Items] for [Purpose]"
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- Question: "[Question Using Primary Keyword]?"
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- Guide: "[Complete/Ultimate] Guide to [Topic]"
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---
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## CONTENT ANGLE REQUIREMENTS
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Each idea must have a **unique angle**:
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✓ Different content structure types across ideas
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✓ Different major topics (not just minor keyword variations)
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✓ Different target intents (informational, commercial, navigational)
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✓ Different depth levels (overview vs deep-dive)
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✓ No duplicate section topics across ideas in same cluster
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✓ **Completely different keyword sets targeting distinct subtopics**
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---
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## GENERATING 3-7 IDEAS
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**Decision criteria for idea count:**
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- **3 ideas**: Narrow, focused cluster with 1-3 major subtopics
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- **4-5 ideas**: Moderate cluster with 4-5 major subtopics
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- **6-7 ideas**: Broad cluster with 6+ distinct major subtopics
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**What counts as a major topic difference:**
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✓ List building vs Platform selection vs Campaign strategy (MAJOR - generate separate ideas)
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✓ "Best tools" vs "Top platforms" (MINOR variation - combine into one idea)
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✓ Email automation vs Email design vs Email analytics (MAJOR - generate separate ideas)
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✓ "How to write emails" vs "Email writing tips" (MINOR variation - combine into one idea)
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---
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## QUALITY CHECKS
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Before finalizing, verify:
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- ✓ 3-7 ideas per cluster (based on major topic count)
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- ✓ Each idea targets a distinct major topic
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- ✓ Each idea has unique content_structure type
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- ✓ Title includes 1-2 primary focus keywords
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- ✓ 1-2 primary_focus_keywords listed per idea
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- ✓ 2-3 covered_keywords listed per idea (3-5 total keywords maximum)
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- ✓ **Primary focus keywords appear in title AND at least 2 H2 headings**
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- ✓ **Each idea covers completely different keywords from cluster**
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- ✓ Content angles represent major topic differences
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- ✓ AI determined optimal section count for each idea
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- ✓ AI determined word count based on sections and keywords
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- ✓ Valid JSON format
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---
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## OUTPUT FORMAT
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Return ONLY valid JSON with no comments or explanations.
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The "outline" object should be simple and high-level - just enough to guide the content generator. The actual detailed structure, formatting, H3 subsections, lists, tables, etc. will be handled by the content generation system.
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---
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## WHAT NOT TO INCLUDE
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❌ Detailed H3 subsections (content generator handles this)
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❌ Specific formatting instructions (paragraph/list/table details)
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❌ Word count per section (content generator calculates)
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❌ Detailed content descriptions (keep coverage notes brief)
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❌ HTML structure (content generator outputs HTML)
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❌ Introduction hook text (content generator writes this)
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❌ Hardcoded section counts (AI determines optimal count)
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❌ Hardcoded word count ranges (AI determines based on content needs)
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Keep outlines strategic and high-level. Let the content generation system handle tactical execution.',
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description = 'Generates 3-7 content ideas per cluster with titles, outlines, primary_focus_keywords, and covered_keywords. Outputs JSON with cluster IDs for proper linking.',
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variables = '["[IGNY8_CLUSTERS]", "[IGNY8_CLUSTER_KEYWORDS]"]',
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version = version + 1,
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last_updated = NOW()
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WHERE prompt_type = 'ideas';
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-- Verify the update
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SELECT prompt_type, version, description, variables, LEFT(prompt_value, 100) as prompt_preview, last_updated
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FROM igny8_global_ai_prompts
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WHERE prompt_type = 'ideas';
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@@ -200,6 +200,21 @@ export default function ClusterDetail() {
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</div>
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)}
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{/* Keywords Section */}
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{cluster.keywords_count > 0 && (
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<div className="mt-4 pt-4 border-t border-gray-200 dark:border-gray-700">
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<div className="text-sm font-medium text-gray-700 dark:text-gray-300 mb-2">Keywords in this Cluster</div>
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<div className="text-sm text-gray-500 dark:text-gray-400">
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<Link
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to={`/planner/keywords?cluster=${cluster.id}`}
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className="text-brand-600 hover:text-brand-700 dark:text-brand-400 dark:hover:text-brand-300 underline"
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>
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View {cluster.keywords_count} keyword{cluster.keywords_count !== 1 ? 's' : ''} →
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</Link>
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</div>
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</div>
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)}
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<div className="mt-4 pt-4 border-t border-gray-200 dark:border-gray-700 flex items-center gap-4">
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{cluster.sector_name && (
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<Badge variant="light" color="info">
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Reference in New Issue
Block a user