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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 1•Chapter 2

Key Features of Langsmith for Evaluation

Summary

The AI is built to summarize YouTube transcripts, outputting results in a defined JSON structure while adhering to specified guidelines for brevity and content focus.

Concept Check

0/5

What primary feature does LangSmith use for evaluating LLM responses?

How does LangSmith enhance evaluation of chain outputs?

Which LangSmith tool is key for performance metrics?

In LangSmith, what facilitates A/B testing for models?

What ensures reproducibility in LangSmith evaluations?

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