Structured Output from LLMs Using Pydantic (Beginner’s Guide)

#langchain #python #langgraph #coding #aiagents #programming #pydantic #aiagents In this video, I explain how to extract structured, reliable output from Large Language Models (LLMs) using Pydantic. We start with a simple schema to demonstrate the basics of structured output, then progressively move to a real-world, production-ready example using deeply nested Pydantic models. *What you’ll learn in this video:* - Why structured output is critical when working with LLMs - How to define strict schemas using Pydantic BaseModel - Using Optional fields and Field descriptions to guide LLM responses - Building nested schemas for complex data extraction - Extracting structured data like: - Display specifications - Performance and chipset details - Camera systems (rear and front) - Battery, software, and connectivity specs - Best practices for enforcing consistency and reliability in LLM responses This approach is essential when building: - AI agents - RAG pipelines - LLM-powered APIs - Production AI systems that must not break due to malformed responses If you are an AI Engineer, LLM Engineer, or Python developer building with modern LLM frameworks, this technique is a must-know. If you’re interested in learning more about AI Agents, Multi-Agent Systems, RAG pipelines, and real-world LLM applications, subscribe to my YouTube channel. *Check the code on GitHub:* https://github.com/CodeByFelix/AI-Agent-Tutorial *Connect with me* *LinkedIn:* https://www.linkedin.com/in/felix-ibeamaka/ *X:* https://x.com/Electronics__ *Facebook:* https://www.facebook.com/felixibeamaka/ *Instagram:* https://www.instagram.com/electroni6_247/ *TikTok:* https://www.tiktok.com/@UClF6nEUxzBRYcd8vjQOenyA *Join My WhatsApp Group:* https://chat.whatsapp.com/C9mZeTCLiNQAe4yxCTmVy4 #aiengineering