How to Get Structured Output from Any LLM in 5 Min

You asked an LLM to extract contact info from an email. It returned a wall of text instead of clean data. Now you're writing regex to parse a response that changes format every time. There's a better way. PydanticAI's output_type parameter forces any LLM to return typed, validated data -- no parsing required. The Code import asyncio from pydantic import BaseModel , Field from pydantic_ai import Agent class ContactInfo ( BaseModel ): """ Structured contact details extracted from text. """ name : str = Field ( description = " Full name of the person " ) email : str = Field ( description = " Email address " ) company : str = Field ( description = " Company or organization " ) role : str = Field ( description = " Job title or role " ) agent = Agent ( ' openai:gpt-4o ' , output_type = ContactInfo , instructions = ' Extract contact information from the provided text. ' , ) raw_text = """ Hey, just met Sarah Chen at the DevTools Summit. She ' s the VP of Engineering at Acme Corp. Her email is