Chapter 30: Pydantic and Generics
Modern AI applications require robust data validation. LLM outputs need validation before use, configuration systems need type safety across environments, and API integrations require structured data contracts. Two powerful tools address these needs: Pydantic (runtime validation that catches bad data) and Generics (static type safety that catches errors before runtime).
This chapter teaches advanced type safety and data validation for AI-native Python development through Pydantic (runtime validation) and Generics (static type safety). You'll learn to validate LLM outputs, build type-safe containers, and combine both patterns in a production-quality Config Manager. This chapter equips you with Pydantic V2 for runtime validation, modern PEP 695 Generic syntax (Python 3.14+), and production patterns for config management and AI output validation.
By the end, you'll build a Type-Safe Config Manager capstone—a production-quality configuration system that's a portfolio-worthy project you can use in your own Python applications.
🎯 Before You Begin
What You'll Learn
By the end of this chapter, you will be able to:
- Pydantic Data Validation — CREATE Pydantic models with nested validation and custom validators, APPLY Pydantic to validate LLM-generated JSON outputs, HANDLE validation errors gracefully in production code
- Generic Type Safety Patterns — WRITE generic functions and classes using TypeVar and PEP 695 syntax, ANALYZE when Generics improve type safety vs simpler approaches
- Integration Patterns — INTEGRATE Pydantic validation with Generic containers, EVALUATE tradeoffs between Pydantic, TypedDict, and dataclasses
- AI-Native Application — VALIDATE AI agent outputs (LLM JSON, structured data), EXPLAIN how validation fits into AI-native development workflow