Get in Touch

Course Outline

Introduction to LangChain

  • Overview of LangChain and its objectives
  • Configuring the development environment

Understanding Large Language Models (LLMs)

  • Differences between LLMs and traditional models
  • Capabilities and limitations of LLMs

LangChain Components and Architecture

  • Key components of LangChain
  • Understanding the architecture and workflow

Integrating LangChain with LLMs

  • Linking LangChain with LLMs like GPT-4
  • Developing chains for specific tasks

Building Modular Applications

  • Creating modular components with LangChain
  • Reusing components across various 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

Troubleshooting

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

Summary and Next Steps

Requirements

  • Foundational knowledge of Python programming
  • Awareness of AI concepts and large language models

Target Audience

  • Software Developers
  • Software Engineers
  • AI Enthusiasts
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories