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Duration 14 hours
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
