LangChain: Building AI-Powered Applications Training Course
LangChain serves as an open-source framework engineered to streamline the creation of applications leveraging large language models (LLMs).
This instructor-led live training, available online or on-site, is tailored for intermediate developers and software engineers looking to construct AI-driven applications with the LangChain framework.
Upon completion of this training, participants will be equipped to:
- Grasp the core concepts and structural components of LangChain.
- Connect LangChain with large language models (LLMs) such as GPT-4.
- Develop modular AI applications using LangChain.
- Resolve frequent challenges encountered in LangChain applications.
Course Structure
- Engaging lectures and open discussions.
- Extensive exercises and practical drills.
- Real-world implementation within a live lab setting.
Customization Options
- For tailored training requirements, please reach out to us to schedule.
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.
Open Training Courses require 5+ participants.
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