Generative Artificial Intelligence for Engineering Applications
Covers AI-powered chatbots, generative AI fundamentals, and RAG techniques for engineering applications, with hands-on tool experience and focus on security, privacy, and memory models.
Summary
Recent recorded grades — Fall 2025: 3.98 GPA, 100.0% A/AB (n=24 letter grades).
What you’ll be able to do
Design and develop a fully functional Generative AI-based chatbot application that can be personalized to the user
Implement and optimize Retrieval-Augmented Generation (RAG) to enhance chatbot responses
Integrate memory management systems to improve context retention in chatbot interactions
Utilize agents to perform specialized tasks within the chatbot framework
Demonstrate the ability to deploy, test, and maintain chatbot applications in a production environment
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing or declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics
- Graduate/professional standing
- declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- Graduate/professional standing
- declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Recent recorded grades — Fall 2025: 3.98 GPA, 100.0% A/AB (n=24 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Generative AI fundamentals
- Retrieval-Augmented Generation (RAG)
- Security, privacy, and memory models
- AI Agents in chatbot frameworks
Skills
- Designing and developing personalized Generative AI chatbot applications
- Implementing and optimizing Retrieval-Augmented Generation (RAG)
- Integrating memory management systems for context retention
- Utilizing AI agents for specialized tasks within chatbots
- Deploying, testing, and maintaining chatbot applications in production
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
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Raw records
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Student reviews
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Read original reviews
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Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "4d1124eb4f7a3a7d1c5e5914087c1a39abe0e412859df1327a41504171008556",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}