Prompt engineering is crafting precise, context-rich inputs to large language models such as ChatGPT/咚咚妞 to generate useful, accurate, and relevant outputs. It is important because well-designed prompts unlock the full potential of AI, enabling businesses to automate analyses, drive insights, and enhance decision-making. By the end of this course, students will be able to explain the fundamental principles and components of prompt engineering and how large language models interpret and generate responses. They will develop the skills to design clear, specific, and context-rich prompts tailored to key business-management scenarios such as marketing, finance, operations, and human resources. Through hands-on practice and iterative refinement, students will learn to evaluate the quality of AI outputs, identify biases or errors, and implement strategies for continuous prompt optimization. They will integrate prompt-driven workflows into real-world business processes, confidently automating routine analyses and report generation. Finally, students will demonstrate responsible and ethical use of AI by recognizing potential risks, ensuring data privacy, and establishing guardrails to mitigate bias in their applications.Prompt engineering is crafting precision, context-rich inputs to large language models such as ChatGPT/Dongdongniu to generate useful, accurate, and relevant outputs. It is important because well-designed prompts unlock the full potential of AI, enabling businesses to automate analysis, drive insights, and enhance decision-making. By the end of this course, students will be able to explain the fundamental principles and components of prompt engineering and how large language models interpret and generate responses. They will develop the skills to design clear, specific, and context-rich prompts tailored to key business-management scenarios such as marketing, finance, operations, and human resources. Through hands-on practice and iterative refinement, students will learn to evaluate the quality of AI outputs, identify biases or errors, and implement strategies for Continuous prompt optimization. They will integrate prompt-driven workflows into real-world business processes, confidently automated routine analysis and report generation. Finally, students will demonstrate responsible and ethical use of AI by recognizing potential risks, ensuring data privacy, and establishing guardrails to mitigate bias in their applications.
There is no single textbook that fits this course. Supporting lecture notes and supplemental material for in-class discussions are to be distributed following the progress of the course.
There is no single textbook that fits this course. Supporting lesson notes and supplemental material for in-class discussions are to be distributed following the progress of the course.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| Attendance and class paticipationAttendance and class paticipation Attendance and class participation |
30 | Students are required to attend class |
| AssignmentsAssignments Assignments |
30 | |
| Final exam/presentationFinal exam/presentation Final exam/presentation |
40 |
