Online Archive of University of Virginia Scholarship
Leveraging Retrieval-Augmented Generation to Reduce Decision Fatigue for Home Searching; Examination of IBM Watson’s Failure in the Medical Field65 views
Author
Wang, Rex, School of Engineering and Applied Science, University of Virginia
Advisors
Sherriff, Mark, EN-Comp Science Dept, University of Virginia
Laugelli, Benjamin, EN-Engineering and Society, University of Virginia
Abstract
My STS research and technical projects are mainly connected due to having something to do with artificial intelligence(AI) and its application in real world problems. AI’s main function is to simulate human intelligence in machines and use them to solve an issue at hand. For my technical project, my team and I constructed a network of actors to accomplish the goal of being able to find homes/apartments that meet requirements without going through the hassles of filters like in many popular websites. To help in our development of our product, I was able to learn to take into account what exactly goes into training a successful model from my STS research from the analysis on the failure of creating a stabilized network. Even though both projects seem to not have that much in common, I was able to apply what I had learned from my STS project to my technical project.
Degree
BS (Bachelor of Science)
Keywords
AI; Housing
Language
English
Rights
All rights reserved by the author (no additional license for public reuse)
Wang, Rex. Leveraging Retrieval-Augmented Generation to Reduce Decision Fatigue for Home Searching; Examination of IBM Watson’s Failure in the Medical Field. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2026-05-08, https://doi.org/10.18130/vw1a-jd12.
