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

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Quah Chee Yong: Quah Chee Yong is a ACTA trainer. Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industries A firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners.

Solomon Soh Zhe Hong: Solomon is ACTA certified and has trained and coached over 100 professionals in the area of data science, python programming and coding. Solomon is a Certified AI Engineer Associate by AI Singapore and holds certifications in Alibaba Cloud Architect and Alteryx respectively. Solomon interests include Reinforcement Learning, Natural Language Processing and Time-Series analysis.

Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore.

His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.