LLM with Structured Output

1.1: Introduction
1.2: Technical Architecture
1.3: PyCharm Installation
1.4: Groq Cloud Setup
1.5: Project Setup
1.6: Basic LLM Call
1.7: LLM with Structured Output
1.8: Planner
1.9: AI Playground
1.10: Graph in LangGraph
1.11: Architect Node
1.12: AI Agents Debugger
1.13: Coder Node

2.1: Quiz

LLM with Structured Output

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LLM with Structured Output

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In this lecture, we will explore how to use LangChain's structured output features to ensure the LLM returns data in a consistent, programmatic format using Pydantic models.

Contents

1: Agentic AI Project in LangGraph: Build Lovable Clone

1.1: Introduction

1.2: Technical Architecture

1.3: PyCharm Installation

1.4: Groq Cloud Setup

1.5: Project Setup

1.6: Basic LLM Call

1.7: LLM with Structured Output

1.8: Planner

1.9: AI Playground

1.10: Graph in LangGraph

1.11: Architect Node

1.12: AI Agents Debugger

1.13: Coder Node

2: Final Quiz

2.1: Quiz

LLM with Structured Output

LLM with Structured Output

Practice while you learn with exercise files