Course Description:

This advanced-level course delves into sophisticated techniques for leveraging LLMs in business, including the use of Retrieval-Augmented Generation (RAGs). Participants will learn about the latest advancements in LLM technology, how to implement responsible AI practices, and strategies for maximizing the impact of LLMs in their organizations.

 

Recommended Skills:

• Strong understanding of LLMs and experience in deploying them.
• Proficiency in programming (Python) and machine learning frameworks.
• Familiarity with data science workflows and cloud services.
• OR
• Taken Implementing LLMs in Business Operations
• Taken Data Science

 

What Will Be Gained:

• Expertise in advanced LLM techniques and use cases.
• Knowledge of Retrieval-Augmented Generation (RAG) and its applications.
• Skills to implement responsible AI practices in LLM deployment.
• Strategies for maximizing the ROI of LLM solutions in business.

 

Course Outline:

Day 1: Advanced LLM Techniques

• Overview of State-of-the-Art LLMs and Innovations
• Understanding Transformer Architectures and Model Scaling
• Few-Shot and Zero-Shot Learning
• Implementing Advanced Techniques with LLMs

 

Day 2: Retrieval-Augmented Generation (RAG)

• Introduction to RAG: Concept and Benefits
• Enhancing Information Retrieval and Knowledge Management
• Implementing RAG with an Open-Source Framework
• Integrating RAG with Existing Business Systems

 

Day 3: Responsible Use of LLMs

• Bias Mitigation, Transparency, and Accountability
• Privacy and Security Considerations in LLM Deployment
• Frameworks and Guidelines for Responsible AI (e.g., GDPR, CCPA)
• Ethical Challenges and Solutions in LLM Implementation

 

Day 4: Maximizing Impact and Future Directions

• Strategies for Scaling LLM Solutions Across the Organization
• Measuring ROI and Effectiveness of LLM Deployments
• Multi-Modal Models, Explainability, and Human-AI Collaboration
• Developing a Comprehensive LLM Strategy for a Business Use Case