LLMs for Automated Customer Support

• An understanding of basic programming concepts
• Experience with Python programming is recommended but not required
• Familiarity with basic machine learning concepts is beneficial

Audience

• Customer support professionals
• IT professionals
• Business analysts

Large Language Models (LLMs) are a type of artificial intelligence that processes and generates human-like text, enabling more natural and effective automated customer support.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level customer support and IT professionals who wish to implement LLMs to create responsive and intelligent customer support chatbots.

By the end of this training, participants will be able to:

• Understand the fundamentals and architecture of Large Language Models (LLMs).
• Design and integrate LLMs into customer support systems.
• Enhance the responsiveness and user experience of chatbots.
• Address ethical considerations and ensure compliance with industry standards.
• Deploy and maintain an LLM-based chatbot for real-world applications.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Large Language Models (LLMs)

• Overview of AI in customer support
• Fundamentals of LLMs
• Evolution of chatbots: from simple scripts to AI-driven support

Architecture of LLMs

• Understanding the building blocks of LLMs
• Neural networks and deep learning in LLMs
• Training LLMs: data, algorithms, and computational resources

Implementing LLMs in Chatbots

• Integration strategies for LLMs in existing systems
• Designing conversational flows and user interactions
• Ensuring contextual understanding and coherence

Enhancing Chatbot Responsiveness

• Techniques for real-time response generation
• Handling concurrent conversations
• Personalization and predictive support

User Experience and Interface Design

• Crafting user-friendly chatbot interfaces
• Visual and textual cues for better engagement
• Feedback loops and continuous improvement

Ethical Considerations and Compliance

• Privacy and data security with LLMs
• Ethical use of AI in customer support
• Adhering to industry standards and regulations

Testing and Deployment

• Quality assurance and testing methodologies
• Deployment strategies for scalability and reliability
• Monitoring and maintenance of chatbot systems

Case Studies and Real-world Applications

• Analyzing successful implementations of LLM chatbots
• Lessons learned and best practices
• Future trends and innovations in AI-driven customer support

Project and Assessment

• Designing and building an LLM-based chatbot
• Peer reviews and group discussions
• Final assessment and feedback

Summary and Next Steps