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.


Details

Project status: Active
Type of project: Teaching/Learning
Timeline: January 2023 – Present

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

RAG_How Does It WorkDownload