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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
Troubleshooting
- Identifying common issues in LangChain applications
- Debugging techniques and solutions
Summary and Next Steps
Requirements
- Basic knowledge of Python programming
- Familiarity with AI concepts and large language models
Audience
- Developers
- Software engineers
- AI enthusiasts
14 Hours