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Course Outline

Introduction to LangChain

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

Understanding Large Language Models (LLMs)

  • Comparison between LLMs and 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.
  • Constructing chains tailored 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.
  • Familiarity with artificial intelligence concepts and large language models.

Audience

  • Developers.
  • Software engineers.
  • AI enthusiasts.
 14 Hours

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