{
"agent": 8,
"design_timestamp": "2026-01-04T00:00:00Z",
"design_standard": "OpenAPI Example (examples/openapi/README.md) used as reference for structure and quality",
"overview": {
"total_paths": 7,
"total_examples": 16,
"progression_levels": ["Foundational", "Intermediate", "Advanced", "Expert"],
"key_principle": "Each path builds on previous knowledge with increasing complexity"
},
"learning_paths": {
"path_1_template_author": {
"id": "path_1",
"name": "Template Author (From Scratch to Production)",
"target_audience": "Developers who want to learn ggen fundamentals and create reusable code templates",
"skill_level": "Beginner → Intermediate",
"time_estimate": "4-6 hours total",
"examples_in_order": [
{
"order": 1,
"example": "basic-template-generation",
"time": "15-30 min",
"focus": "Template anatomy and Tera fundamentals",
"what_you_learn": [
"YAML frontmatter structure",
"Tera template syntax (variables, filters, conditionals)",
"Basic variable substitution",
"Template validation",
"Single-file generation"
],
"hands_on": [
"Examine template structure",
"Understand Tera filters (snake_case, pascal_case, etc.)",
"Modify variables and regenerate",
"Create simple custom templates"
],
"success_metrics": [
"Can explain what YAML frontmatter does",
"Can modify template variables and see output change",
"Understand 5+ Tera filters",
"Successfully create a basic Rust module template"
]
},
{
"order": 2,
"example": "ai-template-creation",
"time": "30-45 min",
"focus": "AI-assisted template generation and iteration",
"what_you_learn": [
"ggen ai generate command",
"Using mock mode for cost-free learning",
"Template validation and error detection",
"Iterative improvement workflow",
"Writing effective prompts"
],
"hands_on": [
"Generate templates from descriptions (mock mode)",
"Run validation on generated templates",
"Fix validation errors and re-run",
"Compare hand-written vs AI-generated templates",
"Iterate template descriptions based on feedback"
],
"success_metrics": [
"Can generate valid templates using AI in mock mode",
"Understand validation output and fix errors",
"Write clear prompts that produce usable templates",
"Successfully validate complex template (2+ iterations)"
]
},
{
"order": 3,
"example": "complete-project-generation",
"time": "45-60 min",
"focus": "Multi-file template generation and project structure",
"what_you_learn": [
"Generating complete projects with multiple files",
"Template composition and module organization",
"Conditional generation with if/else logic",
"Loops for generating multiple similar artifacts",
"Project validation and testing"
],
"hands_on": [
"Generate a complete Rust web service",
"Examine generated project structure",
"Modify variables and regenerate entire project",
"Build and test generated code",
"Customize output by editing templates"
],
"success_metrics": [
"Successfully generate and build a complete project",
"All generated code compiles without errors",
"Generated tests pass (integration tests)",
"Can add new module to template and regenerate"
]
},
{
"order": 4,
"example": "microservices-architecture",
"time": "90-120 min",
"focus": "Complex multi-service generation and orchestration",
"what_you_learn": [
"Generating multiple interdependent services",
"Service discovery and communication patterns",
"Marketplace integration for templates",
"Lifecycle management for complex projects",
"Infrastructure code generation (Docker, Kubernetes)"
],
"hands_on": [
"Generate 6 different microservices from templates",
"Configure inter-service communication",
"Generate Docker Compose and Kubernetes configs",
"Build and deploy entire architecture",
"Test end-to-end service interactions"
],
"success_metrics": [
"All 6 services generate and compile successfully",
"Docker Compose deployment works completely",
"Services can communicate and pass integration tests",
"Can regenerate entire architecture with one command"
]
}
],
"prerequisites": [
"Basic familiarity with YAML syntax",
"Understanding of template concepts (variable substitution)",
"Rust basics (for understanding generated code)",
"Command-line comfort"
],
"success_criteria": [
"Create 3+ production-ready templates from scratch",
"Validate templates and fix all errors",
"Generate complete, compilable, testable projects",
"Understand when/how to use AI assistance vs hand-coding",
"Can teach others how to create templates"
],
"next_steps": [
"Explore Path 2 for RDF/semantic modeling",
"Explore Path 3 for full-stack development",
"Contribute templates to ggen marketplace"
]
},
"path_2_rdf_semantic_developer": {
"id": "path_2",
"name": "RDF Semantic Developer (Ontology-First Design)",
"target_audience": "Architects and developers who want to design systems using RDF/Turtle ontologies",
"skill_level": "Intermediate → Advanced",
"time_estimate": "6-8 hours total",
"examples_in_order": [
{
"order": 1,
"example": "basic-template-generation",
"time": "15-30 min",
"focus": "Foundation for understanding code generation",
"what_you_learn": [
"How template variables work",
"Template rendering pipeline",
"Basic Tera syntax",
"Output file generation"
],
"hands_on": [
"Run basic-template-generation example",
"Examine templates to understand structure"
],
"success_metrics": [
"Understand how templates consume variables",
"Can identify where data should come from"
]
},
{
"order": 2,
"example": "openapi",
"time": "60-90 min",
"focus": "RDF ontologies as source of truth, SPARQL queries, synchronized artifacts",
"what_you_learn": [
"RDF/Turtle ontology modeling",
"SPARQL query fundamentals (SELECT)",
"Ontology design patterns",
"SPARQL results mapped to templates",
"Generating multiple synchronized outputs from one ontology",
"ggen.toml manifest structure",
"10-rule generation pipeline"
],
"hands_on": [
"Examine blog-api.ttl ontology structure",
"Study entity and endpoint definitions",
"Review ggen.toml configuration (10 rules)",
"Understand SPARQL queries in manifest",
"Run ggen sync to generate outputs",
"Verify generated OpenAPI, Zod, and JSDoc are synchronized",
"Add new entity to ontology and regenerate",
"Compare golden/ outputs with generated/"
],
"success_metrics": [
"Understand RDF triple structure and meaning",
"Can read and modify ontology definitions",
"Write 3+ SPARQL SELECT queries correctly",
"Generated outputs are 100% synchronized",
"Successfully add entity to ontology and regenerate"
]
},
{
"order": 3,
"example": "fastapi-from-rdf",
"time": "90-120 min",
"focus": "RDF domain models generating complete applications (Python/FastAPI)",
"what_you_learn": [
"Advanced RDF modeling (models, fields, relationships)",
"Complex SPARQL queries (with filtering, aggregation)",
"Multi-file generation from single RDF source",
"Model relationships (one-to-many, many-to-many)",
"Business logic in RDF",
"Generating models, schemas, routes, tests from ontology"
],
"hands_on": [
"Study domain.ttl structure (3 models, 20+ fields)",
"Understand relationship definitions",
"Review api-stack.tmpl (800+ lines)",
"Trace SPARQL queries and their results",
"Generate complete FastAPI project",
"Run generated application with Docker",
"Add new field to domain model and regenerate",
"Verify all 15+ files update consistently"
],
"success_metrics": [
"Generate complete FastAPI application that runs",
"Understand multi-model RDF patterns",
"Write SPARQL queries with relationships",
"Docker Compose application works end-to-end",
"Successfully modify domain and regenerate all files"
]
},
{
"order": 4,
"example": "microservices-architecture",
"time": "120-150 min",
"focus": "Multiple RDF ontologies for microservices architecture",
"what_you_learn": [
"Modular ontology design across services",
"Service-specific domain models",
"Inter-service data contracts in RDF",
"Generating entire microservice architectures",
"RDF-based service discovery",
"Semantic API documentation"
],
"hands_on": [
"Design separate RDF ontologies for 6 services",
"Define relationships between service models",
"Generate all services from unified domain model",
"Implement service discovery using RDF",
"Document API contracts in semantic format",
"Generate Kubernetes deployment manifests from RDF"
],
"success_metrics": [
"Create 6 separate ontologies for microservices",
"All services deploy and communicate correctly",
"Can add new service by adding ontology",
"Semantic documentation auto-generated",
"Architecture is fully queryable with SPARQL"
]
}
],
"prerequisites": [
"Completion of path_1_template_author recommended",
"Understanding of RDF/Turtle syntax (or willingness to learn)",
"SPARQL basics (tutorial provided)",
"Knowledge of semantic web concepts helpful but not required"
],
"success_criteria": [
"Design and implement 3+ RDF ontologies",
"Create 5+ SPARQL queries that extract domain-specific data",
"Generate synchronized artifacts (3+ types) from single ontology",
"Successfully model complex relationships (1:N, M:N) in RDF",
"Build complete system from ontology to running application"
],
"next_steps": [
"Create custom ontologies for your domain",
"Publish ontologies to SPARQL endpoints",
"Explore semantic web standards (OWL, SHACL)",
"Integrate with other RDF tools (Oxigraph, GraphDB)"
]
},
"path_3_full_stack_developer": {
"id": "path_3",
"name": "Full-Stack Developer (Project Generation & Deployment)",
"target_audience": "Web developers who want rapid project generation with complete scaffolding",
"skill_level": "Beginner → Intermediate",
"time_estimate": "3-4 hours total",
"examples_in_order": [
{
"order": 1,
"example": "basic-template-generation",
"time": "15-30 min",
"focus": "Understanding template fundamentals",
"what_you_learn": [
"How templates work",
"Variable substitution",
"Tera filters for code generation"
],
"hands_on": [
"Run example and examine output"
],
"success_metrics": [
"Understand basic template mechanics"
]
},
{
"order": 2,
"example": "complete-project-generation",
"time": "45-60 min",
"focus": "Generating complete, deployable projects",
"what_you_learn": [
"Multi-file project scaffolding",
"REST API generation",
"Data model generation",
"Integration test generation",
"Project customization"
],
"hands_on": [
"Generate complete Rust web service",
"Examine generated project structure",
"Build and test generated code",
"Add custom business logic",
"Deploy locally"
],
"success_metrics": [
"Generate buildable, testable project",
"All tests pass",
"API endpoints work correctly",
"Code compiles and runs"
]
},
{
"order": 3,
"example": "fastapi-from-rdf",
"time": "60-90 min",
"focus": "Full-stack Python/FastAPI application from ontology",
"what_you_learn": [
"Complete full-stack generation (backend + DB + tests)",
"Docker-based deployment",
"Database schema generation",
"API documentation generation",
"Test suite generation"
],
"hands_on": [
"Generate FastAPI project from RDF",
"Run with Docker Compose",
"Test API endpoints",
"Review generated database models",
"Examine test suite",
"Deploy to local Docker"
],
"success_metrics": [
"Generate complete working FastAPI project",
"Docker Compose runs without errors",
"All endpoints respond correctly",
"Database operations work",
"Tests pass successfully"
]
},
{
"order": 4,
"example": "microservices-architecture",
"time": "90-120 min",
"focus": "Complete microservices deployment",
"what_you_learn": [
"Multi-service architecture generation",
"Service-to-service communication",
"Docker Compose orchestration",
"Kubernetes deployment",
"Health checks and monitoring",
"Full DevOps workflow"
],
"hands_on": [
"Generate 6 microservices",
"Deploy with Docker Compose",
"Test inter-service communication",
"Generate Kubernetes manifests",
"Deploy to Kubernetes",
"Monitor running services"
],
"success_metrics": [
"All 6 services generate and compile",
"Docker Compose deployment works",
"All services pass integration tests",
"Can access all service endpoints",
"Kubernetes deployment succeeds"
]
}
],
"prerequisites": [
"Web development experience (Node.js, Python, or Rust)",
"Understanding of REST APIs",
"Docker basics helpful",
"Kubernetes basics helpful for final example"
],
"success_criteria": [
"Generate 3+ complete, production-ready projects",
"All generated projects build and deploy successfully",
"Write integration tests for generated code",
"Deploy projects to Docker/Kubernetes",
"Customize generated projects for specific business requirements"
],
"next_steps": [
"Explore path_2 for RDF-driven design",
"Explore path_4 for DevOps-focused workflows",
"Explore path_5 for AI-assisted generation"
]
},
"path_4_devops_engineer": {
"id": "path_4",
"name": "DevOps Engineer (<60s Deployment Specialist)",
"target_audience": "Infrastructure and DevOps engineers who want rapid deployment automation",
"skill_level": "Intermediate → Advanced",
"time_estimate": "4-5 hours total",
"examples_in_order": [
{
"order": 1,
"example": "basic-template-generation",
"time": "15-30 min",
"focus": "Template fundamentals for infrastructure code",
"what_you_learn": [
"Template-as-code concept",
"Variable substitution for config generation",
"Template validation"
],
"hands_on": [
"Run example",
"Understand how to apply to infrastructure"
],
"success_metrics": [
"Understand templates as infrastructure code"
]
},
{
"order": 2,
"example": "complete-project-generation",
"time": "45-60 min",
"focus": "Build and deployment automation",
"what_you_learn": [
"Dockerfile generation",
"Build configuration generation",
"CI/CD pipeline templates",
"Health check configuration",
"Deployment scripts"
],
"hands_on": [
"Generate project with Dockerfile",
"Build and test Docker image",
"Create deployment scripts",
"Automate build pipeline",
"Validate deployed service"
],
"success_metrics": [
"Generate and build Docker image",
"Deployment succeeds",
"Health checks pass",
"Can reproduce deployment easily"
]
},
{
"order": 3,
"example": "fastapi-from-rdf",
"time": "60-90 min",
"focus": "Complete containerized deployment with databases",
"what_you_learn": [
"Multi-container Docker Compose",
"Database container generation",
"Environment configuration",
"Network setup",
"Volume management"
],
"hands_on": [
"Generate project with docker-compose.yml",
"Deploy entire stack",
"Test service and database communication",
"Check logging and monitoring",
"Scale services"
],
"success_metrics": [
"Docker Compose deployment works completely",
"Database is accessible from service",
"Logs are properly configured",
"Can scale services easily"
]
},
{
"order": 4,
"example": "microservices-architecture",
"time": "120-180 min",
"focus": "Complete microservices deployment and orchestration",
"what_you_learn": [
"Kubernetes manifest generation",
"Multi-service orchestration",
"Service discovery and load balancing",
"Resource management and scaling",
"Monitoring and logging infrastructure",
"<60 second deployment cycles"
],
"hands_on": [
"Generate Kubernetes manifests for 6 services",
"Deploy to Kubernetes cluster",
"Configure service discovery",
"Set up monitoring and logging",
"Implement auto-scaling",
"Measure deployment time (<60 seconds)",
"Test failover and recovery"
],
"success_metrics": [
"Kubernetes deployment deploys all services in <60 seconds",
"All services are discoverable and reachable",
"Load balancing works correctly",
"Can scale services on demand",
"Monitoring and logging show service health",
"Can rollback deployments quickly"
]
}
],
"prerequisites": [
"Docker and Kubernetes knowledge required",
"Understanding of CI/CD concepts",
"Infrastructure as Code experience helpful",
"Command-line comfort with deployment tools"
],
"success_criteria": [
"Generate and deploy 3+ containerized applications",
"Deploy microservices architecture to Kubernetes",
"Achieve <60 second deployment cycles",
"Implement health checks and monitoring",
"Automate rollback and recovery procedures",
"Document deployment procedures"
],
"next_steps": [
"Explore path_2 for RDF-based infrastructure definitions",
"Create custom Kubernetes generators",
"Integrate with your existing CI/CD pipeline",
"Build marketplace packages for your organization"
]
},
"path_5_ai_engineer": {
"id": "path_5",
"name": "AI Engineer (LLM-Assisted Code Generation)",
"target_audience": "Machine learning and AI engineers who want to use AI for code generation",
"skill_level": "Intermediate → Advanced",
"time_estimate": "5-6 hours total",
"examples_in_order": [
{
"order": 1,
"example": "basic-template-generation",
"time": "15-30 min",
"focus": "Understanding what templates can generate",
"what_you_learn": [
"Template structure and syntax",
"What code can be generated",
"Template validation"
],
"hands_on": [
"Run example"
],
"success_metrics": [
"Understand template capabilities"
]
},
{
"order": 2,
"example": "ai-template-creation",
"time": "60-90 min",
"focus": "AI-powered template generation and iteration",
"what_you_learn": [
"ggen ai generate command",
"Mock mode (cost-free learning)",
"Real AI mode with LLMs",
"Prompt engineering for templates",
"Template validation feedback loops",
"Iterative improvement with AI",
"Cost optimization"
],
"hands_on": [
"Generate templates in mock mode",
"Write and refine prompts",
"Validate generated templates",
"Fix validation errors",
"Iterate templates based on feedback",
"Understand when mock vs real AI",
"Cost-benefit analysis"
],
"success_metrics": [
"Generate 5+ valid templates using mock mode",
"Understand validation output",
"Write clear, effective prompts",
"Successfully iterate templates to perfection",
"Can switch between mock and real AI appropriately"
]
},
{
"order": 3,
"example": "comprehensive-rust-showcase",
"time": "90-120 min",
"focus": "AI-generated Rust patterns and architectural examples",
"what_you_learn": [
"Complex Rust patterns from AI",
"Error handling patterns",
"Async/await patterns",
"Testing patterns",
"Architecture patterns",
"Performance optimization patterns"
],
"hands_on": [
"Study AI-generated Rust code",
"Understand pattern choices",
"Modify patterns using AI prompts",
"Generate variations for different use cases",
"Learn architectural best practices"
],
"success_metrics": [
"Understand 5+ Rust patterns from AI examples",
"Can modify patterns for custom use cases",
"Generated code compiles and passes tests"
]
},
{
"order": 4,
"example": "ai-code-generation",
"time": "120-150 min",
"focus": "Full end-to-end AI-assisted code generation workflows",
"what_you_learn": [
"Complete generation workflows",
"Multi-step prompt chains",
"Feedback incorporation",
"Quality metrics for generated code",
"Performance optimization",
"Code review and validation",
"Production deployment of AI-generated code"
],
"hands_on": [
"Generate complete project using AI",
"Review generated code",
"Run generated tests",
"Incorporate feedback",
"Measure code quality metrics",
"Deploy and validate",
"Iterate based on real-world performance"
],
"success_metrics": [
"AI-generated code passes all tests",
"Code quality metrics meet standards",
"Generated project deploys successfully",
"Can handle feedback and regenerate",
"Understand limitations of AI generation"
]
}
],
"prerequisites": [
"Understanding of LLMs and prompt engineering",
"Basic Rust or Python knowledge",
"Experience with code generation concepts",
"Comfort with iterative refinement workflows"
],
"success_criteria": [
"Generate 10+ production-quality templates using AI",
"Understand AI strengths and limitations",
"Create effective prompts for code generation",
"Validate AI-generated code automatically",
"Build quality gates for AI-generated code",
"Integrate AI generation into development workflow"
],
"next_steps": [
"Explore path_2 for structured AI generation (RDF-based)",
"Experiment with different LLM providers",
"Build custom validation for generated code",
"Create organizational template libraries using AI"
]
},
"path_6_rust_specialist": {
"id": "path_6",
"name": "Rust Specialist (Advanced Patterns & Performance)",
"target_audience": "Rust developers who want production-grade code generation with advanced patterns",
"skill_level": "Advanced → Expert",
"time_estimate": "6-8 hours total",
"examples_in_order": [
{
"order": 1,
"example": "basic-template-generation",
"time": "20-30 min",
"focus": "Template generation of Rust code",
"what_you_learn": [
"Tera syntax for Rust code generation",
"Type safety in templates",
"Macro generation",
"Module organization"
],
"hands_on": [
"Examine generated Rust code",
"Understand compilation process"
],
"success_metrics": [
"Generated code compiles successfully"
]
},
{
"order": 2,
"example": "complete-project-generation",
"time": "60-90 min",
"focus": "Complete Rust project with error handling and async patterns",
"what_you_learn": [
"Async/await code generation",
"Error handling with Result types",
"Trait implementations",
"Lifetimes and ownership",
"Testing patterns",
"Performance considerations"
],
"hands_on": [
"Generate complete project",
"Review error handling patterns",
"Study async code generation",
"Analyze generated tests",
"Build and benchmark",
"Profile performance"
],
"success_metrics": [
"Generated code compiles without warnings",
"All tests pass",
"Performance meets SLO targets",
"No unsafe code blocks"
]
},
{
"order": 3,
"example": "comprehensive-rust-showcase",
"time": "90-120 min",
"focus": "Advanced Rust patterns and architectural choices",
"what_you_learn": [
"Builder patterns",
"RAII patterns",
"Trait objects vs generics",
"Memory optimization",
"Concurrency patterns",
"Zero-copy techniques",
"Macros and procedural macros"
],
"hands_on": [
"Study each pattern implementation",
"Understand trade-offs",
"Benchmark pattern variants",
"Implement custom variations",
"Write integration tests"
],
"success_metrics": [
"Understand trade-offs between 5+ patterns",
"Can implement patterns from scratch",
"Code passes clippy without warnings",
"Benchmarks show optimal performance"
]
},
{
"order": 4,
"example": "microservices-architecture",
"time": "120-150 min",
"focus": "Production-grade microservices with advanced Rust patterns",
"what_you_learn": [
"Service architecture patterns",
"Error propagation across services",
"Async distributed systems",
"Concurrency in microservices",
"Zero-cost abstractions",
"Performance optimization at scale",
"Testing distributed systems"
],
"hands_on": [
"Generate microservices",
"Analyze error handling",
"Profile service communication",
"Load test services",
"Implement custom error types",
"Optimize hot paths",
"Test failure scenarios"
],
"success_metrics": [
"All services compile with zero warnings",
"Services handle errors gracefully",
"Performance benchmarks meet targets",
"Can debug complex async issues",
"Services scale horizontally"
]
}
],
"prerequisites": [
"Advanced Rust knowledge (lifetime management, trait objects, macros)",
"Experience with async/await and tokio",
"Understanding of performance profiling",
"Knowledge of distributed systems concepts"
],
"success_criteria": [
"Generate production-grade Rust services",
"Achieve zero compiler warnings",
"Understand and explain generated patterns",
"Optimize generated code for performance",
"Extend ggen with custom Rust patterns",
"Contribute to ggen codebase"
],
"next_steps": [
"Contribute custom templates to ggen",
"Explore ggen-core internals",
"Build ggen plugins for domain-specific patterns",
"Create benchmarking infrastructure for generated code"
]
},
"path_7_architect": {
"id": "path_7",
"name": "System Architect (End-to-End Design)",
"target_audience": "Architects who want to design systems from ontology through deployment",
"skill_level": "Advanced → Expert",
"time_estimate": "8-10 hours total",
"examples_in_order": [
{
"order": 1,
"example": "openapi",
"time": "90-120 min",
"focus": "Ontology-first API design",
"what_you_learn": [
"RDF/OWL ontology modeling",
"API contract definition in RDF",
"Synchronized API documentation",
"SPARQL query design for APIs",
"Type safety across full stack"
],
"hands_on": [
"Design complete API ontology",
"Create SPARQL queries",
"Generate OpenAPI specs",
"Validate API contracts"
],
"success_metrics": [
"Ontology covers all API requirements",
"Generated OpenAPI spec is complete",
"All artifacts are synchronized"
]
},
{
"order": 2,
"example": "fastapi-from-rdf",
"time": "120-150 min",
"focus": "RDF-driven full-stack architecture",
"what_you_learn": [
"Domain model design in RDF",
"Database schema from ontology",
"API implementation from RDF",
"Test generation from specs",
"Documentation from ontology",
"Technology selection from RDF"
],
"hands_on": [
"Design complete domain ontology",
"Define 3+ entity types with relationships",
"Generate complete stack",
"Verify all layers match ontology",
"Add new domain requirements and regenerate"
],
"success_metrics": [
"Complete system generates from single ontology",
"Database, API, tests all aligned",
"Can add features by modifying ontology only",
"No manual code changes needed"
]
},
{
"order": 3,
"example": "microservices-architecture",
"time": "150-180 min",
"focus": "Microservices architecture from ontology to deployment",
"what_you_learn": [
"Service decomposition strategies",
"Semantic service contracts",
"Data consistency patterns",
"Service-to-service communication design",
"Deployment topology design",
"Monitoring and observability architecture"
],
"hands_on": [
"Design system architecture in RDF",
"Define 6+ services with contracts",
"Generate all services",
"Design inter-service communication",
"Create deployment manifests",
"Implement observability",
"Design failure handling"
],
"success_metrics": [
"Architecture is fully defined in RDF",
"All services generate consistently",
"Communication patterns are clear",
"Can add new service in 15 minutes",
"System scales to 10+ services"
]
},
{
"order": 4,
"example": "comprehensive-rust-showcase",
"time": "120-150 min",
"focus": "Advanced architectural patterns and trade-offs",
"what_you_learn": [
"Architectural pattern selection",
"Trade-offs between approaches",
"Performance vs maintainability",
"Scalability considerations",
"Team structure implications",
"Technology choices"
],
"hands_on": [
"Study multiple architectural approaches",
"Create decision matrix",
"Design alternatives",
"Document trade-offs",
"Present architecture decisions"
],
"success_metrics": [
"Can articulate 5+ architectural patterns",
"Understand trade-offs deeply",
"Can design appropriate architecture for context",
"Can mentor others on architecture"
]
}
],
"prerequisites": [
"10+ years software engineering experience",
"Understanding of system design",
"Knowledge of RDF/ontologies helpful",
"Microservices architecture experience"
],
"success_criteria": [
"Design complete system architectures in RDF",
"Generate complete systems from ontology",
"Define clear service contracts semantically",
"Create deployment-ready manifests",
"Document decisions in RDF format",
"Train team on ontology-first design"
],
"next_steps": [
"Publish architectural decision records in RDF",
"Create organizational ontologies",
"Build architecture review processes around RDF",
"Establish patterns library"
]
}
},
"missing_examples": [
{
"priority": "HIGH",
"name": "Data Pipeline Generation",
"description": "Generate ETL/ELT pipelines and data processing workflows from ontologies",
"ideal_position": "Between path_2 (RDF) and path_3 (Full-Stack)",
"key_concepts": [
"Data lineage modeling in RDF",
"Transform specification",
"Pipeline orchestration (Airflow, Dagster)",
"Data quality checks",
"Monitoring and logging"
],
"estimated_time": "90-120 min"
},
{
"priority": "HIGH",
"name": "Frontend Code Generation (React/Vue/Angular)",
"description": "Generate complete web frontend from API ontology",
"ideal_position": "After path_3 (Full-Stack Development)",
"key_concepts": [
"Component generation from API schema",
"Form generation from validation rules",
"Type-safe API integration",
"State management",
"Testing frameworks"
],
"estimated_time": "90-120 min"
},
{
"priority": "MEDIUM",
"name": "Mobile App Generation (React Native/Flutter)",
"description": "Generate complete mobile applications from ontology",
"ideal_position": "After Frontend Code Generation example",
"key_concepts": [
"Cross-platform code generation",
"Mobile-specific patterns",
"Native module integration",
"Performance optimization for mobile",
"Testing on device"
],
"estimated_time": "120-150 min"
},
{
"priority": "MEDIUM",
"name": "GraphQL Code Generation",
"description": "Generate GraphQL schemas, resolvers, and clients from RDF",
"ideal_position": "After OpenAPI example as alternative API style",
"key_concepts": [
"GraphQL schema generation from RDF",
"Resolver code generation",
"Client type generation",
"Query optimization",
"Subscription patterns"
],
"estimated_time": "90-120 min"
},
{
"priority": "MEDIUM",
"name": "Database Schema Evolution",
"description": "Generate database migrations and schema versioning",
"ideal_position": "After FastAPI example for database-focused developers",
"key_concepts": [
"Schema version management in RDF",
"Migration script generation",
"Backwards compatibility",
"Data transformation",
"Rollback strategies"
],
"estimated_time": "90-120 min"
},
{
"priority": "MEDIUM",
"name": "Documentation Generation",
"description": "Generate API docs, architecture docs, and user guides from ontology",
"ideal_position": "After any example as capstone",
"key_concepts": [
"Multi-format documentation (HTML, PDF, Markdown)",
"API documentation generation",
"Architecture diagrams from RDF",
"User guides and tutorials",
"Search and indexing"
],
"estimated_time": "60-90 min"
},
{
"priority": "MEDIUM",
"name": "Compliance & Security Code Generation",
"description": "Generate GDPR, HIPAA, PCI-DSS compliance code from ontology",
"ideal_position": "Advanced path for regulated industries",
"key_concepts": [
"Compliance rules in RDF",
"Audit logging generation",
"Data encryption from spec",
"Access control implementation",
"Compliance documentation"
],
"estimated_time": "120-150 min"
},
{
"priority": "LOW",
"name": "Game Development Code Generation",
"description": "Generate game entities, systems, and behaviors from ontology",
"ideal_position": "Specialized path for game developers",
"key_concepts": [
"Entity system generation",
"Physics integration",
"AI behavior trees",
"Game loop patterns",
"Performance optimization"
],
"estimated_time": "120-150 min"
}
],
"path_dependencies": {
"path_1_template_author": {
"depends_on": [],
"enables": [
"path_2_rdf_semantic_developer",
"path_3_full_stack_developer",
"path_5_ai_engineer"
]
},
"path_2_rdf_semantic_developer": {
"depends_on": [
"path_1_template_author"
],
"enables": [
"path_7_architect"
]
},
"path_3_full_stack_developer": {
"depends_on": [
"path_1_template_author"
],
"enables": [
"path_4_devops_engineer"
]
},
"path_4_devops_engineer": {
"depends_on": [
"path_3_full_stack_developer"
],
"enables": []
},
"path_5_ai_engineer": {
"depends_on": [
"path_1_template_author"
],
"enables": [
"path_2_rdf_semantic_developer"
]
},
"path_6_rust_specialist": {
"depends_on": [
"path_1_template_author"
],
"enables": [
"path_7_architect"
]
},
"path_7_architect": {
"depends_on": [
"path_2_rdf_semantic_developer",
"path_6_rust_specialist"
],
"enables": []
}
},
"recommended_structure": {
"directory_layout": {
"examples/": {
"README.md": "Update to reference all 7 learning paths",
"LEARNING_PATHS.md": "NEW: Comprehensive guide to all paths with recommendations",
"path_1_template_author/": "NEW: Directory grouping path 1 examples",
"path_2_rdf_semantic_developer/": "NEW: Directory grouping path 2 examples",
"path_3_full_stack_developer/": "NEW: Directory grouping path 3 examples",
"path_4_devops_engineer/": "NEW: Directory grouping path 4 examples",
"path_5_ai_engineer/": "NEW: Directory grouping path 5 examples",
"path_6_rust_specialist/": "NEW: Directory grouping path 6 examples",
"path_7_architect/": "NEW: Directory grouping path 7 examples",
"core_examples/": "Existing examples that are foundation for all paths",
"missing_examples/": "Templates for examples that should be created"
}
},
"documentation_updates": {
"examples/README.md": {
"add_section": "Learning Paths Overview",
"content": "Quick reference table showing all 7 paths with time estimates and target audiences"
},
"examples/LEARNING_PATHS.md": {
"is_new": true,
"content": "Comprehensive guide with:",
"items": [
"Path selection guide (quiz: which path for you?)",
"Detailed progression for each path",
"Time estimates and prerequisites",
"Success criteria and projects",
"Common mistakes and how to avoid them",
"Career progression guide",
"FAQ section"
]
},
"examples/ONBOARDING.md": {
"is_new": true,
"content": "Quick start guide:",
"items": [
"Environment setup (ggen doctor)",
"Path selection questionnaire",
"First example walkthrough",
"Troubleshooting common issues",
"Community resources"
]
}
},
"example_improvements": {
"all_examples": {
"add": [
"Learning path metadata (which paths use this example)",
"Estimated time to complete",
"Prerequisite checklist",
"Success metrics checklist",
"Next examples after this one",
"Common mistakes section"
]
},
"openapi_example": {
"note": "Already excellent - use as template quality standard",
"improvements": [
"Add 'Quick Assessment' section to test understanding",
"Add video walkthrough reference",
"Add 'Compare with other paths' section"
]
}
},
"progression_metrics": {
"beginner_to_intermediate": [
"Completed 2+ examples",
"Can run ggen sync successfully",
"Can modify templates and regenerate"
],
"intermediate_to_advanced": [
"Completed all examples in a path",
"Created 3+ custom templates",
"Generated production-ready code",
"All generated code passes tests"
],
"advanced_to_expert": [
"Mastered RDF ontology design",
"Generated complete system architectures",
"Contributing to ggen codebase",
"Teaching others effectively"
]
}
},
"recommended_next_steps_for_implementation": {
"phase_1_documentation": [
"Create examples/LEARNING_PATHS.md with detailed path guides",
"Update examples/README.md to include learning paths overview",
"Add path metadata to each existing example README",
"Create examples/ONBOARDING.md for new users"
],
"phase_2_missing_examples": [
"Create 'Frontend Code Generation' example (HIGH priority)",
"Create 'Data Pipeline Generation' example (HIGH priority)",
"Create 'GraphQL Code Generation' example (MEDIUM priority)",
"Create 'Documentation Generation' example (MEDIUM priority)"
],
"phase_3_learning_experience": [
"Add checkpoint/assessment scripts for each path",
"Create video walkthroughs for each example",
"Build interactive path selector (CLI tool)",
"Create community project templates",
"Establish mentorship program"
],
"phase_4_continuous_improvement": [
"Track path completion metrics",
"Gather feedback from learners",
"Update paths based on analytics",
"Add new examples as needed",
"Maintain quality standards"
]
},
"quality_assurance": {
"every_example_must_have": [
"Working code that can be run immediately",
"Clear objectives and success criteria",
"Step-by-step instructions",
"Expected output documented",
"Common mistakes and fixes",
"Next steps for progression",
"Links to related examples"
],
"path_quality_gates": [
"All examples compile without warnings",
"All generated code passes clippy",
"Tests pass 100%",
"Documentation is clear and complete",
"Time estimates are accurate",
"Prerequisites are accurate and complete"
],
"learning_outcome_verification": [
"Can articulate what was learned",
"Can apply knowledge to new problems",
"Can complete practical project",
"Can explain to others",
"Can troubleshoot issues independently"
]
},
"success_metrics_for_learning_paths": {
"individual_learner": [
"Completed at least one full path (6+ hours)",
"Generated 3+ production-ready projects",
"All generated code compiles and tests pass",
"Can explain 5+ key concepts from path",
"Successfully extended example with custom requirements"
],
"organizational": [
"50+ developers completed learning paths",
"10+ internal marketplace packages created",
"Standard templates adopted across teams",
"30% reduction in boilerplate code",
"Developer onboarding time reduced by 50%"
],
"community": [
"100+ public templates contributed",
"10+ community examples created",
"Active forums and Q&A activity",
"Regular community challenges",
"Featured success stories"
]
}
}