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Course Outline

Introduction to LangChain

  • Purpose and scope of LangChain.
  • Preparation of the development environment.

Large Language Models (LLMs) Explained

  • Comparison between LLMs and conventional models.
  • Strengths and constraints of LLMs.

LangChain Architecture and Modules

  • Key components within LangChain.
  • Analyzing the system architecture and workflow.

LangChain Integration with LLMs

  • Linking LangChain to LLMs like GPT-4.
  • Constructing task-specific chains.

Creating Modular Applications

  • Developing modular elements using LangChain.
  • Reutilizing components across various projects.

Practical LangChain Exercises

  • Live coding workshops.
  • Building demo applications with LangChain.

Advanced LangChain Capabilities

  • Investigating higher-level features.
  • Adapting LangChain for complex scenarios.

Best Practices and Patterns

  • Coding standards for LangChain.
  • Design patterns for AI-driven applications.

Troubleshooting

  • Pinpointing general issues in LangChain apps.
  • Debugging methods and corrective solutions.

Conclusion and Future Directions

Requirements

  • Foundational proficiency in Python programming.
  • Awareness of AI principles and large language models.

Target Audience

  • Software Developers.
  • Engineers.
  • AI Practitioners and Enthusiasts.
 14 Hours

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