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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 2•Chapter 3

Integrating Prompts and Models in Langchain

Summary

The message instructs to create summaries of up to 250 words, excluding any mentions of sponsors or unrelated content, and avoiding any introductory statements about the summary itself.

Concept Check

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What is the key component for integrating custom prompts in Langchain?

How does Langchain handle prompt and model integration?

Which method ensures secure prompt-model integration in Langchain?

What role does serialization play in Langchain integrations?

In Langchain, how are prompts optimized for models?

PreviousBuilding Simple Chains with Langchain
NextReal-World Project: Developing a Chatbot with Langchain