Retrieval Augmented Generation Framework
Integrating a RAG framework with LLM model to assist admins in policy comparison and decision makings.
Overview
This project integrates a retrieval augmented generation framework with a large language model (LLM) to enable administrators to compare institutional policies on specific subjects. It enhances the LLM’s capability by providing context-specific data retrieval, making policy comparison more efficient and accurate.
The Team
Principal Investigators
- Dr. Patrick Pennefather, Professor, UBC Theatre and Film
Student Team
- Sol Alban, UX/UI design, Data Visualisation, Wireframe
- Yebin Cho, Project Manager, UI/UX Design, Data Visualization, Front-end Development
- Erin Chong, Research, UX/UI Design, Front-end Development
- Mayako Kruger, Client Relations, UX Research, Data Visualization
- Walker Rout, Development, RAG/LLM integration, Website Backend, Vector Databases, Policy pulling
Gallery
