JSON Schema Factory Workshop
Overview
Interactive workshop demonstrating Factory Pattern for structured LLM output control using Pydantic validation. Learn to get reliable, validated JSON from any LLM provider.
Key Features
Factory Pattern: Dynamic schema selection based on data type
Pydantic Integration: Robust validation and type safety
Multi-Provider Support: OpenAI, Gemini, Anthropic compatibility
Template Method: Standardized prompt generation process
Error Handling: Production-ready validation and recovery
Patterns Demonstrated
Primary Patterns
Factory Method: Choose correct schema based on runtime conditions
Template Method: Standard prompt generation workflow
Supporting Patterns
Strategy: Different validation strategies for different use cases
Adapter: Unified interface across LLM providers
What You'll Learn
Structured Output: Get consistent JSON from LLMs
Schema Design: Build flexible, reusable data models
Validation Patterns: Handle errors gracefully
Provider Abstraction: Write provider-independent code
Production Deployment: Real-world validation strategies
Business Applications
Data Extraction: Extract structured data from documents
API Integration: Reliable data transformation pipelines
User Input Processing: Validate and structure user requests
Report Generation: Consistent data formatting
Multi-System Integration: Standardized data exchange
Advantages Over OpenAI Function Calling
Provider Independence: Works with any LLM
Fine-Grained Control: Custom validation logic
Cost Optimization: Choose cheaper providers for simple tasks
Legacy Integration: Works with existing Pydantic models
🚀 Interactive Implementation
Ready to master structured LLM outputs?
🎯 Workshop Highlights:
Live schema validation demonstrations
Multi-provider comparison testing
Real-world data extraction examples
Error handling and recovery patterns
Performance optimization techniques
💡 Perfect For:
Building reliable data extraction pipelines
Creating provider-independent validation systems
Learning advanced Pydantic patterns
Implementing structured AI workflows
Transform unreliable LLM text into reliable structured data with enterprise-grade validation.
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