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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 3

Real-World Project: Applying Pydantic in AI Data Pipelines

Summary

The main topic discusses key innovations in AI technology, focusing on recent advancements and their potential impacts on various industries. It explores how machine learning algorithms are evolving to handle complex data sets more efficiently, leading to improved accuracy in predictions. Additionally, the summary highlights challenges such as ethical considerations and the need for robust security measures in AI implementation.

Concept Check

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What is the primary role of Pydantic in AI data pipelines?

How does Pydantic handle invalid data in pipelines?

In what way does Pydantic improve AI data serialization?

Why integrate Pydantic with FastAPI in AI projects?

What challenge does Pydantic solve in AI pipelines?

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