ALwrity Prompts - AI Integration Plan
This commit is contained in:
ajaysi
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"""
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Content Generator Prompts Package
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This package contains all the prompt templates and generation logic used by the ContentGenerator class
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for generating various types of LinkedIn content.
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"""
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from .post_prompts import PostPromptBuilder
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from .article_prompts import ArticlePromptBuilder
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from .carousel_prompts import CarouselPromptBuilder
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from .video_script_prompts import VideoScriptPromptBuilder
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from .comment_response_prompts import CommentResponsePromptBuilder
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from .carousel_generator import CarouselGenerator
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from .video_script_generator import VideoScriptGenerator
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__all__ = [
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'PostPromptBuilder',
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'ArticlePromptBuilder',
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'CarouselPromptBuilder',
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'VideoScriptPromptBuilder',
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'CommentResponsePromptBuilder',
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'CarouselGenerator',
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'VideoScriptGenerator'
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]
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"""
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LinkedIn Article Generation Prompts
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This module contains prompt templates and builders for generating LinkedIn articles.
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"""
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from typing import Any
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class ArticlePromptBuilder:
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"""Builder class for LinkedIn article generation prompts."""
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@staticmethod
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def build_article_prompt(request: Any) -> str:
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"""
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Build prompt for article generation.
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Args:
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request: LinkedInArticleRequest object containing generation parameters
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Returns:
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Formatted prompt string for article generation
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"""
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prompt = f"""
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You are a senior content strategist and industry expert specializing in {request.industry}. Create a comprehensive, thought-provoking LinkedIn article that establishes authority, drives engagement, and provides genuine value to professionals in this field.
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TOPIC: {request.topic}
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INDUSTRY: {request.industry}
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TONE: {request.tone}
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TARGET AUDIENCE: {request.target_audience or 'Industry professionals, executives, and thought leaders'}
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WORD COUNT: {request.word_count} words
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CONTENT STRUCTURE:
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- Compelling headline that promises specific value
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- Engaging introduction with a hook and clear value proposition
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- 3-5 main sections with actionable insights and examples
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- Data-driven insights with proper citations
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- Practical takeaways and next steps
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- Strong conclusion with a call-to-action
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CONTENT QUALITY REQUIREMENTS:
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- Include current industry statistics and trends (2024-2025)
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- Provide real-world examples and case studies
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- Address common challenges and pain points
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- Offer actionable strategies and frameworks
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- Use industry-specific terminology appropriately
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- Include expert quotes or insights when relevant
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SEO & ENGAGEMENT OPTIMIZATION:
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- Use relevant keywords naturally throughout the content
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- Include engaging subheadings for scannability
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- Add bullet points and numbered lists for key insights
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- Include relevant hashtags for discoverability
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- End with thought-provoking questions to encourage comments
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VISUAL ELEMENTS:
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- Suggest 2-3 relevant images or graphics
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- Recommend data visualization opportunities
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- Include pull quotes for key insights
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KEY SECTIONS TO COVER: {', '.join(request.key_sections) if request.key_sections else 'Industry overview, current challenges, emerging trends, practical solutions, future outlook'}
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REMEMBER: This article should position the author as a thought leader while providing actionable insights that readers can immediately apply in their professional lives.
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"""
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return prompt.strip()
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"""
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LinkedIn Carousel Generation Module
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This module handles the generation of LinkedIn carousels with all processing steps.
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"""
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from typing import Dict, Any, List
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from datetime import datetime
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from loguru import logger
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from services.linkedin.quality_handler import QualityHandler
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class CarouselGenerator:
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"""Handles LinkedIn carousel generation with all processing steps."""
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def __init__(self, citation_manager=None, quality_analyzer=None):
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self.citation_manager = citation_manager
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self.quality_analyzer = quality_analyzer
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async def generate_carousel(
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self,
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request,
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research_sources: List,
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research_time: float,
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content_result: Dict[str, Any],
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grounding_enabled: bool
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):
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"""Generate LinkedIn carousel with all processing steps."""
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try:
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start_time = datetime.now()
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# Step 3: Add citations if requested
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citations = []
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source_list = None
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if request.include_citations and research_sources:
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# Extract citations from all slides
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all_content = " ".join([slide['content'] for slide in content_result['slides']])
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citations = self.citation_manager.extract_citations(all_content) if self.citation_manager else []
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source_list = self.citation_manager.generate_source_list(research_sources) if self.citation_manager else None
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# Step 4: Analyze content quality
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quality_metrics = None
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if grounding_enabled and self.quality_analyzer:
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try:
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all_content = " ".join([slide['content'] for slide in content_result['slides']])
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quality_handler = QualityHandler(self.quality_analyzer)
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quality_metrics = quality_handler.create_quality_metrics(
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content=all_content,
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sources=research_sources,
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industry=request.industry,
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grounding_enabled=grounding_enabled
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)
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except Exception as e:
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logger.warning(f"Quality analysis failed: {e}")
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# Step 5: Build response
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slides = []
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for i, slide_data in enumerate(content_result['slides']):
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slide_citations = []
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if request.include_citations and research_sources and self.citation_manager:
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slide_citations = self.citation_manager.extract_citations(slide_data['content'])
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slides.append({
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'slide_number': i + 1,
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'title': slide_data['title'],
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'content': slide_data['content'],
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'visual_elements': slide_data.get('visual_elements', []),
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'design_notes': slide_data.get('design_notes'),
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'citations': slide_citations
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})
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carousel_content = {
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'title': content_result['title'],
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'slides': slides,
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'cover_slide': content_result.get('cover_slide'),
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'cta_slide': content_result.get('cta_slide'),
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'design_guidelines': content_result.get('design_guidelines', {}),
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'citations': citations,
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'source_list': source_list,
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'quality_metrics': quality_metrics,
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'grounding_enabled': grounding_enabled
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}
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generation_time = (datetime.now() - start_time).total_seconds()
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# Build grounding status
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grounding_status = {
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'status': 'success' if grounding_enabled else 'disabled',
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'sources_used': len(research_sources),
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'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
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'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
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} if grounding_enabled else None
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return {
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'success': True,
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'data': carousel_content,
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'research_sources': research_sources,
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'generation_metadata': {
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'model_used': 'gemini-2.0-flash-001',
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'generation_time': generation_time,
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'research_time': research_time,
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'grounding_enabled': grounding_enabled
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},
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'grounding_status': grounding_status
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}
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except Exception as e:
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logger.error(f"Error generating LinkedIn carousel: {str(e)}")
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return {
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'success': False,
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'error': f"Failed to generate LinkedIn carousel: {str(e)}"
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}
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"""
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LinkedIn Carousel Generation Prompts
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This module contains prompt templates and builders for generating LinkedIn carousels.
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"""
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from typing import Any
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class CarouselPromptBuilder:
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"""Builder class for LinkedIn carousel generation prompts."""
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@staticmethod
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def build_carousel_prompt(request: Any) -> str:
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"""
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Build prompt for carousel generation.
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Args:
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request: LinkedInCarouselRequest object containing generation parameters
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Returns:
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Formatted prompt string for carousel generation
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"""
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prompt = f"""
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You are a visual content strategist and {request.industry} industry expert. Create a compelling LinkedIn carousel that tells a cohesive story and drives engagement through visual storytelling and valuable insights.
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TOPIC: {request.topic}
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INDUSTRY: {request.industry}
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TONE: {request.tone}
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TARGET AUDIENCE: {request.target_audience or 'Industry professionals and decision-makers'}
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NUMBER OF SLIDES: {request.number_of_slides}
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INCLUDE COVER SLIDE: {request.include_cover_slide}
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INCLUDE CTA SLIDE: {request.include_cta_slide}
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CAROUSEL STRUCTURE & DESIGN:
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- Cover Slide: Compelling headline with visual hook and clear value proposition
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- Content Slides: Each slide should focus on ONE key insight with supporting data
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- Visual Flow: Create a logical progression that builds understanding
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- CTA Slide: Clear next steps and engagement prompts
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CONTENT REQUIREMENTS PER SLIDE:
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- Maximum 3-4 bullet points per slide for readability
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- Include relevant statistics, percentages, or data points
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- Use action-oriented language and specific examples
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- Each slide should be self-contained but contribute to the overall narrative
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VISUAL DESIGN GUIDELINES:
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- Suggest color schemes that match the industry (professional yet engaging)
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- Recommend icon styles and visual elements for each slide
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- Include layout suggestions (text placement, image positioning)
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- Suggest data visualization opportunities (charts, graphs, infographics)
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ENGAGEMENT STRATEGY:
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- Include thought-provoking questions on key slides
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- Suggest interactive elements (polls, surveys, comment prompts)
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- Use storytelling elements to create emotional connection
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- End with clear call-to-action and hashtag suggestions
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KEY INSIGHTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Industry trends, challenges, solutions, and opportunities'}
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REMEMBER: Each slide should be visually appealing, informative, and encourage the viewer to continue reading. The carousel should provide immediate value while building anticipation for the next slide.
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"""
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return prompt.strip()
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"""
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LinkedIn Comment Response Generation Prompts
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This module contains prompt templates and builders for generating LinkedIn comment responses.
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"""
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from typing import Any
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class CommentResponsePromptBuilder:
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"""Builder class for LinkedIn comment response generation prompts."""
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@staticmethod
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def build_comment_response_prompt(request: Any) -> str:
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"""
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Build prompt for comment response generation.
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Args:
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request: LinkedInCommentResponseRequest object containing generation parameters
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Returns:
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Formatted prompt string for comment response generation
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"""
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prompt = f"""
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You are a {request.industry} industry expert and LinkedIn engagement specialist. Create a thoughtful, professional comment response that adds genuine value to the conversation and encourages further engagement.
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ORIGINAL COMMENT: "{request.original_comment}"
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POST CONTEXT: {request.post_context}
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INDUSTRY: {request.industry}
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TONE: {request.tone}
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RESPONSE LENGTH: {request.response_length}
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INCLUDE QUESTIONS: {request.include_questions}
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RESPONSE STRATEGY:
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- Acknowledge the commenter's perspective or question
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- Provide specific, actionable insights or examples
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- Share relevant industry knowledge or experience
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- Encourage further discussion and engagement
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- Maintain professional yet conversational tone
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CONTENT REQUIREMENTS:
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- Start with appreciation or acknowledgment of the comment
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- Include 1-2 specific insights that add value
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- Use industry-specific examples when relevant
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- End with a thought-provoking question or invitation to continue
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- Keep the tone consistent with the original post
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ENGAGEMENT TECHNIQUES:
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- Ask follow-up questions that encourage response
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- Share relevant statistics or data points
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- Include personal experiences or case studies
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- Suggest additional resources or next steps
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- Use inclusive language that welcomes others to join
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PROFESSIONAL GUIDELINES:
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- Always be respectful and constructive
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- Avoid controversial or polarizing statements
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- Focus on building relationships, not just responding
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- Demonstrate expertise without being condescending
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- Use appropriate emojis and formatting for warmth
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REMEMBER: This response should feel like a natural continuation of the conversation, not just a reply. It should encourage the original commenter and others to engage further.
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"""
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return prompt.strip()
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"""
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LinkedIn Post Generation Prompts
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This module contains prompt templates and builders for generating LinkedIn posts.
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"""
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from typing import Any
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class PostPromptBuilder:
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"""Builder class for LinkedIn post generation prompts."""
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@staticmethod
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def build_post_prompt(request: Any) -> str:
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"""
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Build prompt for post generation.
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Args:
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request: LinkedInPostRequest object containing generation parameters
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Returns:
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Formatted prompt string for post generation
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"""
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prompt = f"""
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You are an expert LinkedIn content strategist with 10+ years of experience in the {request.industry} industry. Create a highly engaging, professional LinkedIn post that drives meaningful engagement and establishes thought leadership.
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TOPIC: {request.topic}
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INDUSTRY: {request.industry}
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TONE: {request.tone}
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TARGET AUDIENCE: {request.target_audience or 'Industry professionals, decision-makers, and thought leaders'}
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MAX LENGTH: {request.max_length} characters
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CONTENT REQUIREMENTS:
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- Start with a compelling hook that addresses a pain point or opportunity
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- Include 2-3 specific, actionable insights or data points
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- Use storytelling elements to make it relatable and memorable
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- Include industry-specific examples or case studies when relevant
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- End with a thought-provoking question or clear call-to-action
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- Use professional yet conversational language that encourages discussion
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ENGAGEMENT STRATEGY:
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- Include 3-5 highly relevant, trending hashtags (mix of broad and niche)
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- Use line breaks and emojis strategically for readability
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- Encourage comments by asking for opinions or experiences
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- Make it shareable by providing genuine value
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KEY POINTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Current industry trends, challenges, and opportunities'}
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FORMATTING:
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- Use bullet points or numbered lists for key insights
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- Include relevant emojis to enhance visual appeal
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- Break text into digestible paragraphs (2-3 lines max)
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- Leave space for engagement (don't fill the entire character limit)
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REMEMBER: This post should position the author as a knowledgeable industry expert while being genuinely helpful to the audience.
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"""
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return prompt.strip()
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@@ -0,0 +1,97 @@
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"""
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LinkedIn Video Script Generation Module
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This module handles the generation of LinkedIn video scripts with all processing steps.
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"""
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from typing import Dict, Any, List
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from datetime import datetime
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from loguru import logger
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from services.linkedin.quality_handler import QualityHandler
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class VideoScriptGenerator:
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"""Handles LinkedIn video script generation with all processing steps."""
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def __init__(self, citation_manager=None, quality_analyzer=None):
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self.citation_manager = citation_manager
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self.quality_analyzer = quality_analyzer
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async def generate_video_script(
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self,
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request,
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research_sources: List,
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research_time: float,
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content_result: Dict[str, Any],
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grounding_enabled: bool
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):
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"""Generate LinkedIn video script with all processing steps."""
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try:
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start_time = datetime.now()
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# Step 3: Add citations if requested
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citations = []
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source_list = None
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if request.include_citations and research_sources and self.citation_manager:
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all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
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citations = self.citation_manager.extract_citations(all_content)
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source_list = self.citation_manager.generate_source_list(research_sources)
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# Step 4: Analyze content quality
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quality_metrics = None
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if grounding_enabled and self.quality_analyzer:
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try:
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all_content = f"{content_result['hook']} {' '.join([scene['content'] for scene in content_result['main_content']])} {content_result['conclusion']}"
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quality_handler = QualityHandler(self.quality_analyzer)
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quality_metrics = quality_handler.create_quality_metrics(
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content=all_content,
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sources=research_sources,
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industry=request.industry,
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grounding_enabled=grounding_enabled
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)
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except Exception as e:
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logger.warning(f"Quality analysis failed: {e}")
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# Step 5: Build response
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video_script = {
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'hook': content_result['hook'],
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'main_content': content_result['main_content'],
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'conclusion': content_result['conclusion'],
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'captions': content_result.get('captions'),
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'thumbnail_suggestions': content_result.get('thumbnail_suggestions', []),
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'video_description': content_result.get('video_description', ''),
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'citations': citations,
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'source_list': source_list,
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'quality_metrics': quality_metrics,
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'grounding_enabled': grounding_enabled
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}
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generation_time = (datetime.now() - start_time).total_seconds()
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# Build grounding status
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grounding_status = {
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'status': 'success' if grounding_enabled else 'disabled',
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'sources_used': len(research_sources),
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'citation_coverage': len(citations) / max(len(research_sources), 1) if research_sources else 0,
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'quality_score': quality_metrics.overall_score if quality_metrics else 0.0
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} if grounding_enabled else None
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return {
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'success': True,
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'data': video_script,
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'research_sources': research_sources,
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'generation_metadata': {
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'model_used': 'gemini-2.0-flash-001',
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'generation_time': generation_time,
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'research_time': research_time,
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'grounding_enabled': grounding_enabled
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},
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'grounding_status': grounding_status
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}
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except Exception as e:
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logger.error(f"Error generating LinkedIn video script: {str(e)}")
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return {
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'success': False,
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'error': f"Failed to generate LinkedIn video script: {str(e)}"
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||||
}
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@@ -0,0 +1,75 @@
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"""
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LinkedIn Video Script Generation Prompts
|
||||
|
||||
This module contains prompt templates and builders for generating LinkedIn video scripts.
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
class VideoScriptPromptBuilder:
|
||||
"""Builder class for LinkedIn video script generation prompts."""
|
||||
|
||||
@staticmethod
|
||||
def build_video_script_prompt(request: Any) -> str:
|
||||
"""
|
||||
Build prompt for video script generation.
|
||||
|
||||
Args:
|
||||
request: LinkedInVideoScriptRequest object containing generation parameters
|
||||
|
||||
Returns:
|
||||
Formatted prompt string for video script generation
|
||||
"""
|
||||
prompt = f"""
|
||||
You are a video content strategist and {request.industry} industry expert. Create a compelling LinkedIn video script that captures attention in the first 3 seconds and maintains engagement throughout the entire duration.
|
||||
|
||||
TOPIC: {request.topic}
|
||||
INDUSTRY: {request.industry}
|
||||
TONE: {request.tone}
|
||||
TARGET AUDIENCE: {request.target_audience or 'Industry professionals and decision-makers'}
|
||||
DURATION: {request.video_duration} seconds
|
||||
INCLUDE CAPTIONS: {request.include_captions}
|
||||
INCLUDE THUMBNAIL SUGGESTIONS: {request.include_thumbnail_suggestions}
|
||||
|
||||
VIDEO STRUCTURE & TIMING:
|
||||
- Hook (0-3 seconds): Compelling opening that stops the scroll
|
||||
- Introduction (3-8 seconds): Establish credibility and preview value
|
||||
- Main Content (8-{request.video_duration-5} seconds): 2-3 key insights with examples
|
||||
- Conclusion (Last 5 seconds): Clear call-to-action and engagement prompt
|
||||
|
||||
CONTENT REQUIREMENTS:
|
||||
- Start with a surprising statistic, question, or bold statement
|
||||
- Include specific examples and case studies from the industry
|
||||
- Use conversational, engaging language that feels natural when spoken
|
||||
- Include 2-3 actionable takeaways viewers can implement immediately
|
||||
- End with a question that encourages comments and discussion
|
||||
|
||||
VISUAL & AUDIO GUIDELINES:
|
||||
- Suggest background music style and mood
|
||||
- Recommend visual elements (text overlays, graphics, charts)
|
||||
- Include specific camera angle and movement suggestions
|
||||
- Suggest props or visual aids that enhance the message
|
||||
|
||||
CAPTION OPTIMIZATION:
|
||||
- Write captions that are engaging even without audio
|
||||
- Include emojis and formatting for visual appeal
|
||||
- Ensure captions complement the spoken content
|
||||
- Make captions scannable and easy to read
|
||||
|
||||
THUMBNAIL DESIGN:
|
||||
- Suggest compelling thumbnail text and imagery
|
||||
- Recommend color schemes that match the industry
|
||||
- Include specific design elements that increase click-through rates
|
||||
|
||||
ENGAGEMENT STRATEGY:
|
||||
- Include moments that encourage viewers to pause and think
|
||||
- Suggest interactive elements (polls, questions, challenges)
|
||||
- Create emotional connection through storytelling
|
||||
- End with clear next steps and hashtag suggestions
|
||||
|
||||
KEY INSIGHTS TO COVER: {', '.join(request.key_points) if request.key_points else 'Industry trends, challenges, solutions, and opportunities'}
|
||||
|
||||
REMEMBER: This video should provide immediate value while building the creator's authority. Every second should count toward engagement and viewer retention.
|
||||
"""
|
||||
return prompt.strip()
|
||||
Reference in New Issue
Block a user