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Generative AI (Langchain, Langgraph, Langsmith, Pydantic AI)

4 Units16 Lessons
Unit 1

langsmith

Introduction to Langsmith for AI Tracing
Key Features of Langsmith for Evaluation
Real-World Project: Implementing Langsmith in AI Workflow Monitoring
Advanced Langsmith Techniques for AI Optimization
Unit 2

langchain

Basics of Langchain Framework
Building Simple Chains with Langchain
Integrating Prompts and Models in Langchain
Real-World Project: Developing a Chatbot with Langchain
Enhancing Langchain Chains for Complex AI Tasks
Unit 3

pydantic AI

Fundamentals of Pydantic in AI Data Modeling
Advanced Data Schemas with Pydantic for AI
Real-World Project: Applying Pydantic in AI Data Pipelines
Optimizing Pydantic Models for Generative AI Outputs
Unit 4

langgraph

Introduction to Langgraph Workflows
Real-World Project: Building a Multi-Agent System with Langgraph
Advanced Graph Structures in Langgraph
Unit 3•Chapter 2

Advanced Data Schemas with Pydantic for AI

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What mechanism allows Pydantic to handle recursive models?

How does Pydantic implement custom field validators?

In Pydantic, how are nested schemas defined for AI data?

What ensures field exclusion in Pydantic model output?

Why use Pydantic for AI data schema validation?

PreviousFundamentals of Pydantic in AI Data Modeling
NextReal-World Project: Applying Pydantic in AI Data Pipelines