Course Outline

Introduction to LangChain

  • Overview of LangChain and its purpose
  • Setting up the development environment

Understanding Large Language Models (LLMs)

  • LLMs vs traditional models
  • Capabilities and limitations of LLMs

LangChain Components and Architecture

  • Core components of LangChain
  • Understanding the architecture and workflow

Integrating LangChain with LLMs

  • Connecting LangChain to LLMs like GPT-4
  • Building chains for specific tasks

Building Modular Applications

  • Creating modular components with LangChain
  • Reusing components across different applications

Practical Exercises with LangChain

  • Hands-on coding sessions
  • Developing sample applications using LangChain

Advanced LangChain Features

  • Exploring advanced functionalities
  • Customizing LangChain for complex use cases

Best Practices and Patterns

  • Coding best practices with LangChain
  • Design patterns for AI-powered applications


  • Identifying common issues in LangChain applications
  • Debugging techniques and solutions

Summary and Next Steps


  • Basic knowledge of Python programming
  • Familiarity with AI concepts and large language models


  • Developers
  • Software engineers
  • AI enthusiasts
 14 Hours

Number of participants

Price per participant

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