Large Language Models (LLM): From AI Applications to Project Management for Business Executives - 生產力學院
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Large Language Models (LLM): From AI Applications to Project Management for Business Executives
10016881-01
香港九龍達之路78號
2025-05-21
張小姐 - 27885013 | 余小姐 - 27885029
maggiecheung@hkpc.org
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[只提供英文内容]

Programme Highlights

Unlock the power of Large Language Models in your company! From theory to implementation, master key concepts, latest applications, project management tips, vendor strategies, and security concerns. Anyone without a technical background can learn how to manage an LLM project!

Learning Outcomes

In this hands-on session, you will learn:

  • Understand the fundamentals of Large Language Models (LLMs) and their applications
  • Implement LLM projects effectively, covering the project lifecycle and vendor management
  • Address security considerations such as On-Premise vs. Cloud, privacy, etc

Course Fee

HK$3,000/ HK$2,700*
*Group discount for 2 participants or more

For NITTP applicants: HK$1,000# (Original price: HK$3,000)
#Maximum saving, with the final grant subjects to approval.

This course is an approved New Industrialisation and Technology Training Programme (NITTP) offers up to 2/3 course fee reimbursement upon successful applications. For details: https://nittp.vtc.edu.hk/.​

Date

21 May 2025 (Wed)

Time

09:30 – 17:00

Duration

Total 6 lecture hours

Medium

Cantonese
(supplemented with English terminology and handouts)

Certificate of Achievement

A Certificate of Attendance will be awarded to participants who have completed the course.

Course Structure

1. Introduction to Large Language Models (LLMs)
  • What are LLMs?
  • Brief history and evolution
  • Importance in today’s digital landscape
2. Key concepts and Terminology
  • Natural Language Processing (NLP)
  • Machine Learning and Deep Learning
  • Neural Networks
  • Training, Fine-tuning, and Inference
  • Tokens and Embeddings
3. Applications of LLMs
  • Citizen services and chatbots
  • Document analysis and summarization
  • Policy development and analysis
  • Multilingual communication
  • Cybersecurity and fraud detection
4. Implementing LLM Projects
  • Project lifecycle
  • Needs assessment and goal setting
  • Data collection and preparation
  • Model selection and customization
  • Testing and evaluation
  • Deployment and monitoring
5. Project Management Tips for LLM Initiatives
  • Stakeholder management
  • Risk assessment and mitigation
  • Budget planning and resource allocation
  • Timeline management
  • Change management and user adoption
6. Vendor Management for LLM Projects
  • Types of LLM vendors and service providers
  • Evaluating vendor proposals
  • Negotiating contracts and SLAs
  • Managing vendor relationships
  • Ensuring data privacy and security
7. On-Premise vs. Cloud Considerations
  • Advantages and disadvantages of each approach
  • Security and compliance considerations
  • Infrastructure requirements
  • Cost comparisons
  • Hybrid solutions
8. Ethical Considerations and Governance
  • Bias in LLMs
  • Privacy concerns
  • Transparency and explainability
  • Regulatory compliance (e.g. GDPR, local regulations)
  • Hybrid solutions
9. Future Trends and Opportunities
  • Emerging LLM technologies
  • Potential future applications in government
  • Preparing for the evolving landscape
10. Case Studies and Practical Exercises
  • Real-world examples of LLM implementation in government
  • Group exercises on project planning and vendor selection
  • Hands-on demo of LLM applications (if possible)

Trainer

Kit WONG

Graduated from the City University of Hong Kong. With 15+ years of experience, Kit helps businesses thrive through AI, cloud, and digital marketing solutions. He’s a skilled developer, consultant, and trainer, delivering engaging Generative AI x ChatGPT workshops for diverse audiences, from government, property, to finance. His expertise empowers participants to implement AI solutions and achieve operational excellence.

Matt YIU (Co-trainer)

Matt is an accomplished software engineer with a proven track record of optimizing cloud systems for operational efficiency . He holds a Master of Philosophy degree in Computer Science and Engineering, bringing a deep understanding of complex technical concepts to his training sessions. Matt’s expertise ensures participants gain valuable insights and practical skills in utilizing AI-powered tools.

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