3 Credit Hours

This is an introductory-to-intermediate course focused on applied generative AI. Designed for students with a basic programming background who are interested in building practical AI systems using modern LLM tools.

The course prioritizes:

• Ease of use and accessibility, with no complex setup required
• Hands-on learning through coding notebooks and mini-projects
• Real-world applications of LLMs and intelligent agents

Students will learn how to:

• Use pre-trained LLMs for text generation and reasoning
• Build retrieval-based systems using vector databases
• Develop simple agent workflows that interact with tools
• Create interactive AI applications and demos

Prerequisites

No formal prerequisites required. Recommended skills:

• Basic programming knowledge (Python preferred)
• Familiarity with introductory statistics concepts
• Basic data handling (e.g., CSV files, pandas)
• Problem-solving and logical thinking skills

No prior experience with machine learning or deep learning is required.

Course Objectives

Upon completing this course, students will be able to:

• Use LLMs to build practical AI applications
• Apply prompt engineering techniques effectively
• Develop retrieval-augmented systems for real-world data
• Create simple intelligent agents that interact with external tools
• Build and deploy interactive AI applications
• Understand ethical and responsible AI practices

Course Requirements

• Weekly coding assignments (Google Colab notebooks)
• Hands-on labs and mini-projects
• A final capstone project (individual or group)

Textbooks

• Hugging Face Course (available online)
• Hands-On Large Language Models (O’Reilly, 2024)
• Selected research papers and tutorials (provided during class)

Software Requirements

• Google Colab (primary development environment)
• Python 3 (available via Colab)
• Optional: VS Code for local development
• GitHub account

Created: 04/13/2026.