Course Introduction
This course empowers students to master communication with artificial intelligence. It presents prompt engineering as both a creative art and an analytical science. The curriculum requires no prior coding experience. Instead, it focuses on strategically leveraging Large Language Models (LLMs) to achieve specific goals, enhance productivity, and drive innovative problem-solving.
Methodology: Collaborative Application
The pedagogical approach prioritizes applied learning over pure theory. Every module integrates "Group Activity Labs" to ensure practical mastery. These collaborative sessions are designed to:
Build Practical Skills: Students apply techniques in real-time scenarios.
Foster Peer Learning: Teams exchange insights and diverse perspectives.
Refine Outputs: Students critically analyze and optimize AI-generated content within a group setting.
Attendance and Participation
A minimum attendance rate of 50% is required to pass. However, physical presence is strongly encouraged. The core value of this course lies in the interactive laboratory sessions, where direct engagement creates the most significant learning outcomes.
Course Aims
This course is designed to help students:
Master Effective Communication: Structure prompts to elicit precise and high-quality outputs from LLMs.
Understand and Apply Prompt Patterns: Apply Role-Prompting and Chain-of-Thought techniques to perform complex tasks in a practical, group lab environment.
Reducing Hallucination: Learn how to reduce hallucination while creating the prompts.
Debug and Secure Prompts: Learn to troubleshoot poor outputs and understand ethical/security implications (e.g., Prompt Injection).
Boost Productivity: Apply prompt engineering across academic, creative, and professional disciplines.
Course Modules
The course is divided into 14 key modules, each including a dedicated Group Activity Lab for practical application:
Introduction to LLMs and Prompt Engineering
What is Prompt Engineering? (Core components)
Deepening Interaction with Prompts - Advanced Communication - Final Project
Learning from Prompt Mistakes - HW1
Mastering Prompts: Prompt Patterns I (Structured frameworks)
Mastering Prompts - Prompt Patterns II (Advanced patterns such as AoT, CoT)- HW2
Prompt Engineering for Academic and Creative Writing
Midterm Exam and Practical Assessment
Reducing Hallucination & Debugging Prompts: Why Didn't It Work? (Collaborative troubleshooting)
Model Comparison of LLMs: From Prompts to Insights - HW3
Ethical Considerations in Prompt Engineering & Prompt Engineering Across Disciplines
Next Frontiers in Prompt Engineering & AI Agents
Understanding Prompt Security (Prompt Injection and defense)
Project Presentations and Evaluation