JISARA

Abstract: This study focuses on improving LLM contextual understanding through external context sourcing RAG. In this approach, documents containing text, images, and tables, are transformed into high-dimensional vectors via LLM-generated embeddings, then stored in a vector database. When a user submits a query, the LLM retrieves the most relevant documents from this vector store, enabling a deeper understanding of the query and improving response quality. A comparative analysis—supported by a t-test—indicates that this RAG-based model outperforms typical LLM, demonstrating greater efficiency and accuracy. These research studies are combined in this manuscript to unlock the hidden potentials of LLMs by providing a comprehensive understanding of improving Artificial Intelligence in contextualization for accurate answer generation and engagement. This solution eradicates LLMs’ hallucinations by providing them with the right context and putting machines’ reasoning on the same page as humans. The study also creates a solution to the long challenges of AI and the battle with the spread.

Download this article: JISARA - V19 N2 Page 36.pdf

Recommended Citation: Balogun, E., Wimmer, H., Rebman Jr., C.M., (2026). A Comparative Analysis of an LLM and RAG (Retrrieval Augmented Generation) in a Question and Answer System. Journal of Information Systems Applied Research and Analytics 19(2) pp 36-49. https://doi.org/10.62273/RJMJ2996