Online Archive of University of Virginia Scholarship
Leveraging Retrieval-Augmented Generation to Reduce Decision Fatigue for Home Searching, AI Models in Real Estate: A Sociotechnical Analysis of AI Adoption and Employment Transformation85 views
Author
Song, Alex, School of Engineering and Applied Science, University of Virginia
Advisors
JACQUES, RICHARD, EN-Engineering and Society, University of Virginia
Sherriff, Mark, EN-Comp Science Dept, University of Virginia
Abstract
With the proliferation of AI in diverse and varying applications, both the benefits and problems it brings are felt in every aspect of society today. One such industry affected by new, advanced generative models is online real estate; with so much data, online real estate marketplaces can take advantage of algorithmic machine learning tools to efficiently connect buyers, sellers, and agents. My technical project encompasses the design and deployment of a chatbot that uses retrieval-augmented generation (RAG) to help users find real estate listings with natural language. The ethical concerns surrounding these tools is explored in my STS research paper, which focuses on the privacy, transparency, bias, and accuracy issues inherent to all AI applications and their presence in the online real estate market.
Degree
BS (Bachelor of Science)
Keywords
AI in Real Estate; Retrieval-Augmented Generation; Chatbot
Notes
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Mark Sherriff
STS Advisor: Richard Jacques
Technical Team Members: Rex Wang, Kyle Chau
Language
English
Rights
All rights reserved by the author (no additional license for public reuse)
Song, Alex. Leveraging Retrieval-Augmented Generation to Reduce Decision Fatigue for Home Searching, AI Models in Real Estate: A Sociotechnical Analysis of AI Adoption and Employment Transformation. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2026-05-08, https://doi.org/10.18130/92sx-3n06.
