Pydantic for Structured Data Validation in LLM Workflows

Topic 1: Foundations of Structured Output & Pydantic Basics

Why free-form LLM text is not enough for production systems

What structured output is and why it matters in LLM workflows

Common failure modes in unstructured LLM responses

Introduction to Pydantic

Parsing and validating JSON responses from LLMs

Handling validation errors gracefully

Topic 2: Using Pydantic with LLM APIs & Agent Frameworks

Designing structured schemas for LLM outputs

Prompting LLMs for structured responses

Using Pydantic models directly in API calls

Structured outputs with modern LLM providers

Function calling and tool calling with Pydantic models

Ensuring completeness and correctness before triggering downstream systems

Example: Customer Support Assistant

Extracting ticket details

Validating customer data

Routing requests based on structured output

Topic 3: Advanced Validation Patterns & Production Workflows

Nested models and complex schemas

Enums, unions, and custom validators

Combining structured outputs and tool-calling in agent workflows

Using Pydantic in multi-step LLM pipelines

Defensive programming: validating at every stage

Integrating Pydantic into FastAPI or backend services

How popular frameworks use Pydantic under the hood

Designing robust LLM systems where every step is structured and validated