LangChain: Building AI-Powered Applications Training Course
LangChain is an open-source framework designed to streamline the creation of applications leveraging large language models (LLMs).
This instructor-led live training, available either online or onsite, is tailored for intermediate-level developers and software engineers aiming to build AI-powered applications using the LangChain framework.
Upon completion of this training, participants will be equipped to:
- Grasp the core concepts and components of LangChain.
- Connect LangChain with large language models (LLMs) such as GPT-4.
- Develop modular AI applications using LangChain.
- Address and resolve common challenges within LangChain applications.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Customization Options for the Course
- To arrange a customized training session for this course, please reach out to us.
Course Outline
Introduction to LangChain
- Overview of LangChain and its objectives
- Setting up the development environment
Understanding Large Language Models (LLMs)
- LLMs compared to traditional models
- Capabilities and constraints of LLMs
LangChain Components and Architecture
- Core components of LangChain
- Understanding the architecture and workflow
Integrating LangChain with LLMs
- Linking LangChain to LLMs such as GPT-4
- Constructing 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
- Basic proficiency in Python programming
- Familiarity with AI concepts and large language models
Target Audience
- Developers
- Software engineers
- AI enthusiasts
Open Training Courses require 5+ participants.
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