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AI is quickly becoming a core tool across nearly every field.

This course helps students from any major learn to use AI thoughtfully, critically, and creatively. You will practice prompt engineering, learn how large language models behave, build small AI-powered tools, and develop the vocabulary to evaluate AI systems instead of treating them like magic.

What You'll Learn

  • How to design prompts as a form of computational thinking: breaking problems down, testing results, and improving through iteration
  • How large language models work under the hood, including tokens, context, training, and hallucination
  • How to build useful AI tools such as custom chatbots, workflow automations, and retrieval-augmented generation (RAG) systems
  • How to evaluate AI outputs with rubrics, examples, and real criteria instead of vibes
  • How to reason about bias, privacy, academic integrity, and responsible AI use across disciplines

Example Projects

  • Build a chatbot for a class, club, research question, or professional domain
  • Create an AI tool that automates a real task and measure whether it actually works
  • Design a RAG system that retrieves information from real sources before answering

Who This Course Is For

Open to all majors. No prior AI experience is required. Some programming experience is helpful, but the course is designed as an introduction to computational thinking through modern AI systems.

By the end of the semester, you'll have a portfolio of projects and the confidence to apply these techniques in internships, research, or your own work.

If you're interested in learning more about how this applies to your major, dig deeper with the following guides:

Note: this course was previously listed as CSC-225: Prompt Engineering for Large Language Models. It is now being offered as a themed section of CSC-105.

Prompt Engineering for...

Questions? Contact Professor Johnson.