756 lines
29 KiB
Python
756 lines
29 KiB
Python
"""
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World-Class Persona System for AI Analysis
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Simulates real-world team allocation with domain-specific experts from top companies.
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"""
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from typing import Dict, List, Optional, Tuple
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import re
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# ============================================================================
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# CODE ANALYSIS PERSONAS (for AI Analysis Service)
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# ============================================================================
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CODE_ANALYSIS_PERSONAS = {
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# BACKEND DOMAINS
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"backend_api": {
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"role": "Senior Backend API Architect",
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"companies": ["Google", "Amazon", "Stripe"],
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"expertise": ["REST APIs", "GraphQL", "gRPC", "API Gateway", "Microservices"],
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"experience_years": "18+",
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"achievements": [
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"Designed APIs at Google Cloud Platform handling 10M+ requests/day",
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"Built scalable API infrastructure at Amazon AWS serving millions of customers",
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"Led API architecture at Stripe processing billions in transactions"
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],
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"detection_keywords": ["api", "controller", "route", "endpoint", "service", "rest", "graphql"],
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"focus_areas": [
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"API design patterns and best practices",
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"API versioning and backward compatibility",
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"Rate limiting and throttling strategies",
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"API documentation quality",
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"Security vulnerabilities in API endpoints"
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]
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},
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"backend_database": {
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"role": "Senior Database Architect",
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"companies": ["Amazon", "Oracle", "MongoDB"],
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"expertise": ["SQL", "NoSQL", "Database Design", "Query Optimization", "Data Modeling"],
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"experience_years": "20+",
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"achievements": [
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"Designed database systems at Amazon handling petabytes of data",
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"Optimized databases at Oracle for enterprise-scale applications",
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"Built distributed databases at MongoDB for global scale"
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],
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"detection_keywords": ["database", "db", "model", "schema", "migration", "repository", "orm", "query"],
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"focus_areas": [
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"Database schema design and normalization",
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"Query performance and optimization",
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"Data integrity and constraints",
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"Indexing strategies",
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"Transaction management"
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]
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},
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"backend_business": {
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"role": "Senior Backend Business Logic Architect",
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"companies": ["Microsoft", "Salesforce", "SAP"],
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"expertise": ["Business Logic", "Domain Modeling", "Design Patterns", "Service Layer"],
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"experience_years": "17+",
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"achievements": [
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"Architected business logic systems at Microsoft for enterprise applications",
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"Designed domain models at Salesforce for CRM platforms",
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"Built service layers at SAP for ERP systems"
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],
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"detection_keywords": ["service", "business", "logic", "domain", "entity", "dto", "handler"],
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"focus_areas": [
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"Code organization and structure",
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"Design patterns implementation",
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"Business logic maintainability",
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"Domain modeling quality",
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"Service layer architecture"
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]
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},
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# FRONTEND DOMAINS
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"frontend_ui": {
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"role": "Senior Frontend UI Architect",
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"companies": ["Apple", "Meta", "Netflix"],
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"expertise": ["React", "Vue", "Angular", "Component Design", "UI/UX"],
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"experience_years": "15+",
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"achievements": [
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"Built user interfaces at Apple used by millions daily",
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"Led React architecture at Meta (Facebook) for large-scale applications",
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"Designed performance-optimized UIs at Netflix for 200M+ users"
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],
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"detection_keywords": ["component", "ui", "view", "page", "jsx", "tsx", "vue", "template"],
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"focus_areas": [
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"Component architecture and reusability",
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"User experience and accessibility",
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"UI performance optimization",
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"Design system consistency",
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"Responsive design implementation"
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]
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},
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"frontend_state": {
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"role": "Senior Frontend State Management Architect",
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"companies": ["Meta", "Netflix", "Airbnb"],
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"expertise": ["Redux", "Zustand", "Context API", "State Management", "Data Flow"],
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"experience_years": "14+",
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"achievements": [
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"Architected state management at Meta for complex applications",
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"Designed data flow patterns at Netflix for real-time updates",
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"Built state systems at Airbnb for booking platforms"
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],
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"detection_keywords": ["store", "state", "redux", "context", "recoil", "zustand", "mobx"],
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"focus_areas": [
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"State architecture and patterns",
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"Data flow optimization",
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"State synchronization",
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"Performance in state updates",
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"State management best practices"
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]
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},
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# DEVOPS DOMAINS
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"devops_ci_cd": {
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"role": "Senior DevOps CI/CD Architect",
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"companies": ["Google", "Netflix", "Uber"],
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"expertise": ["CI/CD", "Jenkins", "GitHub Actions", "GitLab CI", "Deployment Automation"],
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"experience_years": "12+",
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"achievements": [
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"Built CI/CD pipelines at Google handling 50K+ deployments/day",
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"Designed deployment systems at Netflix for zero-downtime releases",
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"Architected automation at Uber for global scale"
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],
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"detection_keywords": ["ci", "cd", "pipeline", "jenkins", "github-actions", "gitlab", "deploy"],
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"focus_areas": [
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"CI/CD pipeline efficiency",
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"Deployment strategy and automation",
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"Quality gates and testing",
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"Rollback strategies",
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"Build optimization"
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]
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},
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"devops_infrastructure": {
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"role": "Senior Infrastructure Architect",
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"companies": ["Amazon", "Google", "Microsoft"],
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"expertise": ["Kubernetes", "Docker", "Terraform", "Cloud Infrastructure", "Scalability"],
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"experience_years": "16+",
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"achievements": [
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"Designed infrastructure at Amazon AWS for global scale",
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"Built container orchestration at Google for millions of containers",
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"Architected cloud systems at Microsoft Azure with 99.99% uptime"
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],
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"detection_keywords": ["docker", "kubernetes", "terraform", "infrastructure", "cloud", "aws", "gcp", "azure"],
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"focus_areas": [
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"Infrastructure scalability",
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"System reliability and uptime",
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"Cost optimization",
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"Security in infrastructure",
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"Monitoring and observability"
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]
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},
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# SECURITY DOMAINS
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"security_engineer": {
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"role": "Senior Security Engineer",
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"companies": ["Google", "Microsoft", "Cloudflare"],
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"expertise": ["Security", "Vulnerability Assessment", "Penetration Testing", "Security Architecture"],
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"experience_years": "15+",
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"achievements": [
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"Led security initiatives at Google protecting billions of users",
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"Designed security systems at Microsoft for enterprise applications",
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"Built security infrastructure at Cloudflare for DDoS protection"
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],
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"detection_keywords": ["security", "auth", "encryption", "jwt", "oauth", "ssl", "tls", "cors"],
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"focus_areas": [
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"Security vulnerabilities and threats",
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"Authentication and authorization",
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"Data encryption and protection",
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"Security best practices",
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"Compliance and regulations"
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]
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},
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# DATA DOMAINS
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"data_engineer": {
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"role": "Senior Data Engineer",
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"companies": ["Google", "Netflix", "Uber"],
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"expertise": ["Data Pipelines", "ETL", "Big Data", "Data Warehousing", "Spark"],
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"experience_years": "13+",
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"achievements": [
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"Built data pipelines at Google processing petabytes daily",
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"Designed ETL systems at Netflix for real-time analytics",
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"Architected data infrastructure at Uber for millions of rides"
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],
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"detection_keywords": ["data", "pipeline", "etl", "warehouse", "spark", "hadoop", "kafka"],
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"focus_areas": [
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"Data architecture and pipelines",
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"ETL performance and optimization",
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"Data quality and validation",
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"Scalability in data processing",
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"Data governance"
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]
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},
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"ml_engineer": {
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"role": "Senior ML/AI Engineer",
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"companies": ["OpenAI", "Anthropic", "Google DeepMind"],
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"expertise": ["Machine Learning", "Deep Learning", "AI Systems", "Model Training"],
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"experience_years": "12+",
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"achievements": [
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"Developed ML models at OpenAI for language understanding",
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"Built AI systems at Anthropic for safety-critical applications",
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"Designed training pipelines at Google DeepMind for large-scale models"
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],
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"detection_keywords": ["ml", "ai", "model", "training", "neural", "tensorflow", "pytorch", "learning"],
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"focus_areas": [
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"ML model architecture",
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"Training pipeline optimization",
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"Model performance and accuracy",
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"Scalability in ML systems",
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"AI safety and ethics"
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]
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},
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# TESTING DOMAINS
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"qa_automation": {
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"role": "Senior QA Automation Architect",
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"companies": ["Google", "Microsoft", "Amazon"],
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"expertise": ["Test Automation", "Selenium", "Cypress", "Jest", "Testing Strategy"],
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"experience_years": "14+",
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"achievements": [
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"Built test automation at Google for thousands of test cases",
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"Designed testing frameworks at Microsoft for enterprise software",
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"Architected QA systems at Amazon for e-commerce platforms"
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],
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"detection_keywords": ["test", "spec", "jest", "cypress", "selenium", "pytest", "testing"],
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"focus_areas": [
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"Test coverage and quality",
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"Automation strategy",
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"Test maintainability",
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"Performance testing",
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"Testing best practices"
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]
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},
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"performance_engineer": {
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"role": "Senior Performance Engineer",
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"companies": ["Google", "Netflix", "Amazon"],
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"expertise": ["Performance Optimization", "Load Testing", "Profiling", "Scalability"],
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"experience_years": "16+",
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"achievements": [
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"Optimized systems at Google handling billions of requests",
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"Designed performance solutions at Netflix for streaming at scale",
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"Built performance infrastructure at Amazon for peak traffic"
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],
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"detection_keywords": ["performance", "load", "stress", "benchmark", "profiling", "optimization"],
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"focus_areas": [
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"Performance bottlenecks",
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"Optimization strategies",
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"Scalability concerns",
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"Resource utilization",
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"Performance testing"
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]
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},
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# CTO (for synthesis)
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"cto": {
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"role": "Chief Technology Officer",
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"companies": ["Google", "Microsoft", "Amazon"],
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"expertise": ["Strategic Planning", "System Architecture", "Team Leadership", "Technology Strategy"],
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"experience_years": "25+",
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"achievements": [
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"Former VP of Engineering at Google, leading teams of 500+ engineers",
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"CTO at Microsoft Azure, responsible for cloud infrastructure strategy",
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"Strategic advisor at Amazon Web Services for enterprise architecture"
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],
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"focus_areas": [
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"Strategic technology insights",
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"System-wide risk assessment",
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"Architectural recommendations",
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"Cross-domain synthesis",
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"Executive-level analysis"
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]
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}
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}
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# ============================================================================
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# DOCUMENT ANALYSIS PERSONAS (for Multi-Document Upload Service)
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# ============================================================================
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DOCUMENT_ANALYSIS_PERSONAS = {
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"technical_doc_analyst": {
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"role": "Senior Technical Documentation Analyst",
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"companies": ["Google", "Stripe", "Microsoft"],
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"expertise_domain": "technical documentation and API specifications",
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"document_types": ["API docs", "technical specs", "developer guides"],
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"experience_years": "15+",
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"achievements": [
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"Analyzed technical documentation at Google for millions of API integrations",
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"Led documentation analysis at Stripe for developer experience",
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"Mapped technical relationships at Microsoft for enterprise systems"
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],
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"focus_areas": [
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"Technical dependencies and relationships",
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"System integration points",
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"API contract relationships",
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"Technical process flows",
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"Code-to-documentation mappings"
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],
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"visual_focus_areas": [
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"API flow diagrams",
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"System integration diagrams",
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"Technical architecture flows"
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],
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"detection_keywords": ["api", "technical", "specification", "documentation", "guide", "reference", "developer"]
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},
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"business_process_analyst": {
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"role": "Senior Business Process Analyst",
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"companies": ["McKinsey", "Deloitte", "Accenture"],
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"expertise_domain": "business processes and stakeholder requirements",
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"document_types": ["business requirements", "user stories", "business plans"],
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"experience_years": "18+",
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"achievements": [
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"Analyzed business processes at McKinsey for Fortune 500 companies",
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"Led process mapping at Deloitte for enterprise transformations",
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"Mapped stakeholder relationships at Accenture for global projects"
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],
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"focus_areas": [
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"Business process flows",
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"Requirement dependencies",
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"Stakeholder impact chains",
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"Business decision consequences",
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"Organizational impact analysis"
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],
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"visual_focus_areas": [
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"Business process diagrams",
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"Stakeholder impact maps",
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"Decision flowcharts"
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],
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"detection_keywords": ["business", "requirement", "stakeholder", "user story", "process", "workflow", "business plan"]
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},
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"system_architecture_analyst": {
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"role": "Senior System Architecture Document Analyst",
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"companies": ["Google", "Amazon", "Microsoft"],
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"expertise_domain": "system architecture and design documents",
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"document_types": ["architecture docs", "design documents", "system designs"],
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"experience_years": "20+",
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"achievements": [
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"Analyzed architecture documents at Google for large-scale distributed systems",
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"Mapped system relationships at Amazon for cloud infrastructure",
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"Led architecture analysis at Microsoft for enterprise solutions"
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],
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"focus_areas": [
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"Architecture relationships",
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"Component dependencies",
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"System interaction flows",
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"Design decision impacts",
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"Scalability relationships"
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],
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"visual_focus_areas": [
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"Architecture diagrams",
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"Component interaction diagrams",
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"System dependency maps"
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],
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"detection_keywords": ["architecture", "design", "system", "component", "diagram", "architectural"]
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},
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"requirements_analyst": {
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"role": "Senior Requirements & Specification Analyst",
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"companies": ["IBM", "Oracle", "SAP"],
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"expertise_domain": "requirements and functional specifications",
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"document_types": ["requirements docs", "functional specs", "feature specs"],
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"experience_years": "17+",
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"achievements": [
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"Analyzed requirements at IBM for enterprise software implementations",
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"Mapped specifications at Oracle for database systems",
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"Led requirement analysis at SAP for ERP platforms"
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],
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"focus_areas": [
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"Requirement dependencies",
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"Feature relationships",
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"Specification impacts",
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"Change propagation",
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"Implementation dependencies"
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],
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"visual_focus_areas": [
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"Requirement traceability diagrams",
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"Feature dependency maps",
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"Impact analysis charts"
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],
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"detection_keywords": ["requirement", "specification", "feature", "functional", "traceability", "spec"]
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},
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"process_flow_analyst": {
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"role": "Senior Process Flow Analyst",
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"companies": ["Amazon", "Netflix", "Uber"],
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"expertise_domain": "operational processes and workflows",
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"document_types": ["process docs", "workflows", "operational manuals"],
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"experience_years": "14+",
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"achievements": [
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"Analyzed processes at Amazon for fulfillment operations",
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"Mapped workflows at Netflix for content delivery",
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"Led process analysis at Uber for ride-sharing operations"
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],
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"focus_areas": [
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"Process step relationships",
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"Workflow dependencies",
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"Sequential cause-effects",
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"Decision impacts",
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"Operational dependencies"
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],
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"visual_focus_areas": [
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"Process flowcharts",
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"Workflow diagrams",
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"Decision trees",
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"Operational flow maps"
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],
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"detection_keywords": ["process", "workflow", "procedure", "operational", "manual", "step", "flow"]
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},
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"visual_architecture_analyst": {
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"role": "Senior Visual Architecture Analyst",
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"companies": ["Google", "Microsoft", "Apple"],
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"expertise_domain": "visual diagrams and architecture drawings",
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"document_types": ["diagrams", "flowcharts", "architecture drawings"],
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"experience_years": "16+",
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"achievements": [
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"Analyzed visual diagrams at Google for complex system mappings",
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"Mapped architecture drawings at Microsoft for enterprise solutions",
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"Led visual analysis at Apple for product architecture"
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],
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"focus_areas": [
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"Visual relationship extraction",
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"Diagram dependency mapping",
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"Flow analysis",
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"Component interactions",
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"Visual pattern recognition"
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],
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"visual_focus_areas": [
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"All types of visual diagrams",
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"Architecture drawings",
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"Flowcharts and process diagrams",
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"Component and sequence diagrams"
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],
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"detection_keywords": ["diagram", "flowchart", "visual", "drawing", "chart", "map", "image"]
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}
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}
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# ============================================================================
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# DOCUMENT TYPE MAPPING
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# ============================================================================
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DOCUMENT_PERSONA_MAPPING = {
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# Technical Documents
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"api_documentation": "technical_doc_analyst",
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"technical_specification": "technical_doc_analyst",
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"code_documentation": "technical_doc_analyst",
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"developer_guide": "technical_doc_analyst",
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# Business Documents
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"business_requirements": "business_process_analyst",
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"user_stories": "business_process_analyst",
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"business_plan": "business_process_analyst",
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"product_specification": "business_process_analyst",
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"stakeholder_document": "business_process_analyst",
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# Architecture Documents
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"architecture_document": "system_architecture_analyst",
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"system_design": "system_architecture_analyst",
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"design_document": "system_architecture_analyst",
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"technical_design": "system_architecture_analyst",
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# Requirements Documents
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"requirements_document": "requirements_analyst",
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"functional_specification": "requirements_analyst",
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"feature_specification": "requirements_analyst",
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# Process Documents
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"process_document": "process_flow_analyst",
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"workflow_document": "process_flow_analyst",
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"procedure_guide": "process_flow_analyst",
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"operational_manual": "process_flow_analyst",
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# Visual/Diagram Documents
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"architecture_diagram": "visual_architecture_analyst",
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"flowchart": "visual_architecture_analyst",
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"sequence_diagram": "visual_architecture_analyst",
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"component_diagram": "visual_architecture_analyst",
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"process_diagram": "visual_architecture_analyst",
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"system_diagram": "visual_architecture_analyst",
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}
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# ============================================================================
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# PERSONA ALLOCATION FUNCTIONS
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# ============================================================================
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|
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def allocate_code_persona(file_path: str, content: str, chunk_type: str = "module") -> Dict:
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"""
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Intelligently allocates code analysis persona based on file path, content, and type.
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Returns persona config with prompt context.
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"""
|
|
file_lower = file_path.lower()
|
|
content_lower = content.lower()[:2000] if content else "" # Sample content
|
|
|
|
# Score each persona based on detection rules
|
|
persona_scores = {}
|
|
|
|
for persona_id, persona_config in CODE_ANALYSIS_PERSONAS.items():
|
|
if persona_id == "cto": # Skip CTO for individual analysis
|
|
continue
|
|
|
|
score = 0
|
|
detection_keywords = persona_config.get("detection_keywords", [])
|
|
|
|
# Check file path (higher weight)
|
|
for keyword in detection_keywords:
|
|
if keyword in file_lower:
|
|
score += 15
|
|
|
|
# Check content (medium weight)
|
|
for keyword in detection_keywords:
|
|
if keyword in content_lower:
|
|
score += 8
|
|
|
|
# Check chunk type
|
|
if chunk_type and chunk_type.lower() in detection_keywords:
|
|
score += 10
|
|
|
|
# Domain-specific boosts
|
|
if "test" in file_lower and "qa" in persona_id:
|
|
score += 20
|
|
if "security" in file_lower and "security" in persona_id:
|
|
score += 20
|
|
if "performance" in file_lower and "performance" in persona_id:
|
|
score += 20
|
|
|
|
if score > 0:
|
|
persona_scores[persona_id] = score
|
|
|
|
# Select top persona
|
|
if persona_scores:
|
|
selected_id = max(persona_scores, key=persona_scores.get)
|
|
return CODE_ANALYSIS_PERSONAS[selected_id]
|
|
|
|
# Default fallback to backend business logic
|
|
return CODE_ANALYSIS_PERSONAS.get("backend_business", {})
|
|
|
|
|
|
def allocate_document_persona(file_path: str, content: str, file_type: str = "text") -> Dict:
|
|
"""
|
|
Intelligently allocates document analysis persona based on file path, content, and type.
|
|
Returns persona config for document analysis.
|
|
"""
|
|
file_lower = file_path.lower()
|
|
content_lower = content.lower()[:2000] if content else ""
|
|
|
|
# Check if it's an image/diagram
|
|
if file_type == "image" or any(ext in file_lower for ext in [".png", ".jpg", ".jpeg", ".gif", ".svg", ".pdf"]):
|
|
return DOCUMENT_ANALYSIS_PERSONAS.get("visual_architecture_analyst", {})
|
|
|
|
# Score each persona based on detection rules
|
|
persona_scores = {}
|
|
|
|
for persona_id, persona_config in DOCUMENT_ANALYSIS_PERSONAS.items():
|
|
score = 0
|
|
detection_keywords = persona_config.get("detection_keywords", [])
|
|
|
|
# Check file path (higher weight)
|
|
for keyword in detection_keywords:
|
|
if keyword in file_lower:
|
|
score += 15
|
|
|
|
# Check content (medium weight)
|
|
for keyword in detection_keywords:
|
|
if keyword in content_lower:
|
|
score += 8
|
|
|
|
# Check document type mapping
|
|
for doc_type, mapped_persona in DOCUMENT_PERSONA_MAPPING.items():
|
|
if doc_type in file_lower and mapped_persona == persona_id:
|
|
score += 20
|
|
|
|
if score > 0:
|
|
persona_scores[persona_id] = score
|
|
|
|
# Select top persona
|
|
if persona_scores:
|
|
selected_id = max(persona_scores, key=persona_scores.get)
|
|
return DOCUMENT_ANALYSIS_PERSONAS[selected_id]
|
|
|
|
# Default fallback to technical doc analyst
|
|
return DOCUMENT_ANALYSIS_PERSONAS.get("technical_doc_analyst", {})
|
|
|
|
|
|
def get_cto_persona() -> Dict:
|
|
"""Returns CTO persona for synthesis and high-level analysis."""
|
|
return CODE_ANALYSIS_PERSONAS.get("cto", {})
|
|
|
|
|
|
# ============================================================================
|
|
# PROMPT BUILDING FUNCTIONS
|
|
# ============================================================================
|
|
|
|
def build_persona_intro(persona: Dict, assignment_context: str = "", analysis_type: str = "code") -> str:
|
|
"""
|
|
Builds persona introduction section for prompts.
|
|
Works for both code and document analysis.
|
|
"""
|
|
if not persona:
|
|
return ""
|
|
|
|
role = persona.get("role", "Senior Engineer")
|
|
companies = persona.get("companies", [])
|
|
experience = persona.get("experience_years", "15+")
|
|
achievements = persona.get("achievements", [])
|
|
focus_areas = persona.get("focus_areas", [])
|
|
|
|
# Build company background
|
|
company_bg = ""
|
|
if companies:
|
|
company_bg = f"- Previously worked at {', '.join(companies[:2])}"
|
|
if len(companies) > 2:
|
|
company_bg += f" and {companies[2]}"
|
|
|
|
# Build achievements section
|
|
achievements_text = ""
|
|
if achievements:
|
|
achievements_text = "\n".join([f"- {achievement}" for achievement in achievements[:2]])
|
|
|
|
# Build focus areas
|
|
focus_text = ""
|
|
if focus_areas:
|
|
focus_text = "\n".join([f"- {focus}" for focus in focus_areas[:5]])
|
|
|
|
intro = f"""You are {role} with {experience} years of experience.
|
|
|
|
COMPANY BACKGROUND:
|
|
{company_bg}
|
|
|
|
KEY ACHIEVEMENTS:
|
|
{achievements_text}
|
|
|
|
YOUR ASSIGNMENT:
|
|
{assignment_context if assignment_context else 'Analyze the provided code/document for quality, issues, and recommendations.'}
|
|
|
|
YOUR FOCUS AREAS:
|
|
{focus_text}
|
|
|
|
---
|
|
"""
|
|
return intro
|
|
|
|
|
|
def build_code_analysis_persona_prompt(base_prompt: str, persona: Dict,
|
|
assignment_context: str = "") -> str:
|
|
"""
|
|
Enhances code analysis prompt with persona context.
|
|
"""
|
|
if not persona:
|
|
return base_prompt
|
|
|
|
persona_intro = build_persona_intro(persona, assignment_context, "code")
|
|
return persona_intro + base_prompt
|
|
|
|
|
|
def build_document_analysis_persona_prompt(base_prompt: str, persona: Dict,
|
|
document_type: str = "document",
|
|
assignment_context: str = "") -> str:
|
|
"""
|
|
Enhances document analysis prompt with persona context.
|
|
"""
|
|
if not persona:
|
|
return base_prompt
|
|
|
|
role = persona.get("role", "Senior Analyst")
|
|
companies = persona.get("companies", [])
|
|
expertise_domain = persona.get("expertise_domain", "document analysis")
|
|
experience = persona.get("experience_years", "15+")
|
|
achievements = persona.get("achievements", [])
|
|
focus_areas = persona.get("focus_areas", [])
|
|
|
|
company_bg = f"- Previously worked at {', '.join(companies[:2])}" if companies else ""
|
|
achievements_text = "\n".join([f"- {achievement}" for achievement in achievements[:2]]) if achievements else ""
|
|
focus_text = "\n".join([f"- {focus}" for focus in focus_areas[:5]]) if focus_areas else ""
|
|
|
|
intro = f"""You are {role}, a specialist in analyzing {expertise_domain} with {experience} years of experience.
|
|
|
|
COMPANY BACKGROUND:
|
|
{company_bg}
|
|
|
|
KEY ACHIEVEMENTS:
|
|
{achievements_text}
|
|
|
|
YOUR SPECIALIZATION:
|
|
You excel at identifying:
|
|
{focus_text}
|
|
|
|
YOUR ASSIGNMENT:
|
|
{assignment_context if assignment_context else f'Analyze this {document_type} to extract causal relationships and dependencies.'}
|
|
|
|
---
|
|
"""
|
|
return intro + base_prompt
|
|
|
|
|
|
def build_cto_synthesis_prompt(base_prompt: str, team_findings: List[Dict] = None) -> str:
|
|
"""
|
|
Builds CTO-level synthesis prompt with team allocation context.
|
|
"""
|
|
cto_persona = get_cto_persona()
|
|
|
|
if not cto_persona:
|
|
return base_prompt
|
|
|
|
role = cto_persona.get("role", "Chief Technology Officer")
|
|
companies = cto_persona.get("companies", [])
|
|
experience = cto_persona.get("experience_years", "25+")
|
|
achievements = cto_persona.get("achievements", [])
|
|
focus_areas = cto_persona.get("focus_areas", [])
|
|
|
|
company_bg = f"- Former VP of Engineering at {companies[0] if companies else 'Google'}, leading teams of 500+ engineers"
|
|
if len(companies) > 1:
|
|
company_bg += f"\n- CTO at {companies[1]}, responsible for cloud infrastructure strategy"
|
|
|
|
achievements_text = "\n".join([f"- {achievement}" for achievement in achievements[:2]]) if achievements else ""
|
|
focus_text = "\n".join([f"- {focus}" for focus in focus_areas[:5]]) if focus_areas else ""
|
|
|
|
team_allocation = ""
|
|
if team_findings:
|
|
team_allocation = "\n\nTEAM ALLOCATION:\n"
|
|
team_allocation += "You have allocated your expert team to analyze different domains:\n"
|
|
for finding in team_findings[:5]:
|
|
domain = finding.get("domain", "unknown")
|
|
team_allocation += f"- {domain}: Expert analysis completed\n"
|
|
|
|
intro = f"""You are {role} with {experience} years of experience.
|
|
|
|
COMPANY BACKGROUND:
|
|
{company_bg}
|
|
|
|
KEY ACHIEVEMENTS:
|
|
{achievements_text}
|
|
{team_allocation}
|
|
|
|
YOUR ROLE:
|
|
You have received this project and allocated your expert team to analyze different domains.
|
|
Now, synthesize all team findings into strategic recommendations.
|
|
|
|
YOUR FOCUS AREAS:
|
|
{focus_text}
|
|
|
|
---
|
|
"""
|
|
return intro + base_prompt
|
|
|