GRM-179-195 A Retrieval-Augmented Generation (RAG) System for Bible Question Answering Using Scriptural Text and Commentary

GRM-179-195 A Retrieval-Augmented Generation (RAG) System for Bible Question Answering Using Scriptural Text and Commentary

https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php

This project develops and evaluates a question-answering (QA) system designed to address theological and interpretive questions about the New Testament. It uses a Retrieval-Augmented Generation (RAG) framework that integrates a pretrained large language model with a structured knowledge base consisting of public-domain Berean Standard Bible (BSB) New Testament and New Testament commentaries. User queries are embedded to retrieve semantically relevant passages, which are then supplied as contextual input for answer generation. The system is evaluated based on retrieval quality, answer faithfulness, and comparison to ground truth. Performance is benchmarked against a baseline BM25 keyword retrieval system without commentary, demonstrating that commentary-augmented semantic retrieval improves interpretive accuracy and reduces hallucinated responses.