Introduction - Python for AI Beginner Course

Pydantic brings runtime data validation to Python using type hints.

The problem with Python

Python is dynamically typed. This means you can do this:

age = 25
age = "twenty-five" 
age = ["25", 25, None]

No errors. Python doesn’t care. This flexibility is great for quick scripts. But it becomes a nightmare when you’re building real applications, especially when working with external data.

When things go wrong

Imagine you’re building an API that receives user data:

def create_user(data):
    user_id = data["id"]
    email = data["email"]
    age = data["age"]
    
    # Later in your code...
    birth_year = 2025 - age  # What if age is "25" instead of 25?

Your API receives JSON from the outside world. You expect:

{"id": 1, "email": "[email protected]", "age": 25}

But the API sends:

{"id": 1, "email": null, "age": "unknown"}

Your code crashes.

The real-world impact

In any application, you’re constantly working with:

API responses - External services return whatever they want
User input - Never trust user data
Configuration - Environment variables are always strings
Database records - Data can be missing or malformed

Without validation, bugs hide until production. With Pydantic, they surface immediately.

What Pydantic does

Pydantic validates data at runtime. You define what your data should look like, and Pydantic ensures it matches:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    email: str
    age: int

# Valid data - works fine
user = User(id=1, email="[email protected]", age=25)

# Invalid data - fails immediately with clear error
user = User(id=1, email=None, age="unknown")

When validation fails, you get a clear error message telling you exactly what went wrong:

2 validation errors for User
email
  Input should be a valid string
age
  Input should be a valid integer, unable to parse string as an integer

The problem is caught at the source.

Pydantic in the Python ecosystem

Pydantic is everywhere:

FastAPI uses Pydantic for request/response validation
Django Ninja uses Pydantic for API schemas
SQLModel combines Pydantic with SQLAlchemy
Most modern Python frameworks rely on Pydantic under the hood

Learning Pydantic is about writing reliable, type-safe Python code.

Installation

Install Pydantic in your project with either pip or uv:

pip install pydantic

uv add pydantic

Why learn Pydantic

Next to Python basics, Pydantic is one of the most important libraries to learn. Especially in the era of agentic coding.

More robust applications: Strict Pydantic models catch bugs before they reach production. You define what valid data looks like, and Pydantic enforces it at runtime.
AI agents understand your code better: When you define clear Pydantic models, AI coding assistants can understand your data structures. Models act as guardrails that guide both humans and AI.
Required for agentic systems: Building AI agents that call tools, process data, or interact with APIs? You need structured output. Pydantic models define what the AI should return, making agent responses predictable and type-safe.
You’ll encounter it everywhere: If you’re interested in Python for AI, you’ll run into Pydantic constantly. FastAPI, LangChain, OpenAI SDKs, and most AI frameworks use Pydantic under the hood.

Learn more

Practice & Exercises

To reinforce what you’ve learned, practice with these interactive notebooks:

Follow-Along Practice

Practice the basics of dynamic typing limitations, handling runtime errors, and using Pydantic models for basic validation and error handling.💻 VS Code | 🚀 Colab | 📥 Download

Practice Exercises

Test your knowledge with exercises defining simple Pydantic models and handling ValidationErrors.💻 VS Code | 🚀 Colab | 📥 Download

What’s next?

Before diving deeper into Pydantic, you need to understand type hints. They’re the foundation Pydantic builds on.

Type Hints

Learn about Python Type Hints, the foundation Pydantic builds on.

​The problem with Python

​When things go wrong

​The real-world impact

​What Pydantic does

​Pydantic in the Python ecosystem

​Installation

​Why learn Pydantic

​Learn more

​Practice & Exercises