CourseGenix

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 3

Real-World Project: Implementing Langsmith in AI Workflow Monitoring

Summary

Instructions specify a summary of 250 words or less, excluding sponsors and unrelated topics, without any introductory statements.

Concept Check

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What is the main purpose of Langsmith in AI workflows?

Monitoring and tracing AI processes

Real-time error detection

Monitoring and tracing AI processes

Data encryption services

How does Langsmith enhance AI workflow monitoring?

Through automated model training

Via user interface improvements

By providing detailed tracing logs

What challenge arises when implementing Langsmith?

Increasing computational costs

Integrating with existing systems

Reducing data accuracy

Integrating with existing systems

In Langsmith implementation, what is key for monitoring?

Real-time data analytics

Static code analysis

Manual logging processes

Why use Langsmith in AI projects?

To generate synthetic data

For efficient workflow debugging

For basic data storage

For efficient workflow debugging

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