LangChain: Building AI-Powered Applications Training Course
LangChain is an open-source framework built to streamline the creation of applications leveraging large language models (LLMs).
This instructor-led, live training (available online or onsite) is tailored for intermediate-level developers and software engineers looking to construct AI-driven applications using the LangChain framework.
Upon completion of this training, participants will be capable of:
- Grasping the core principles and components of LangChain.
- Seamlessly integrating LangChain with large language models such as GPT-4.
- Constructing modular AI applications leveraging LangChain.
- Resolving typical challenges encountered in LangChain-based applications.
Training Format
- Engaging lectures paired with active discussion.
- Extensive exercises and practical practice sessions.
- Practical implementation within a live-lab environment.
Customization Options for the Course
- For inquiries regarding customized training for this course, please reach out to us to arrange.
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
- Foundational knowledge of 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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