Sample Syllabus

Course Description

This course, Prompt Engineering for Large Language Models (LLMs) (DS312), focuses on techniques for iteratively designing and testing effective prompts for large language models. Large language models are powerful tools for natural language processing tasks, but designing effective prompts is crucial for obtaining accurate and meaningful results. In this course, students will learn how to leverage the power of large language models through prompt engineering.

Learning Objectives

By the end of this course, students will be able to:

• Understand the principles of prompt engineering for large language models.
• Design and test effective prompts for various natural language processing tasks.
• Evaluate the performance of prompts using large language models.
• Apply prompt engineering techniques to real-world natural language processing tasks.

Course Structure

The course content will be presented through a series of lectures, hands-on workshops, and projects. Students will be evaluated through assignments, a final project, and class participation.

Assignments

Throughout the semester, students will be given assignments that will require them to design and test prompts for large language models. These assignments will allow students to practice their prompt engineering skills.

Final Project

The final project for this course will involve designing and testing a set of prompts for a specific natural language processing task. Students will have the opportunity to apply all the skills learned throughout the course to develop effective prompts and evaluate their performance using large language models. The final project will count towards a significant portion of the overall course grade.

Schedule

The following is a general outline of the topics covered in the course:

Week	Topic
1	Introduction to Large Language Models and Prompt Engineering
2	Understanding LLMs: GPT-3, BERT, and Others
3	Designing Effective Prompts: Strategies and Best Practices
4	Testing Prompts: Metrics and Evaluation
5	Iterative Prompt Design: Refinement and Optimization
6	Prompt Engineering for Text Generation Tasks
7	Prompt Engineering for Text Classification Tasks
8	Prompt Engineering for Question Answering Tasks
9	Prompt Engineering for Language Translation Tasks
10	Advanced Prompt Engineering Techniques
11	Case Studies in Prompt Engineering
12	Final Project Presentations and Course Reflection