Course Outline

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

Requirements

  • 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
 14 Hours

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