Speaker
MICHAIL MAKRIS
(UNIPI)
Description
Retrieval-Augmented Generation (RAG) offers a robust way to enhance large language models (LLMs) with domain-specific knowledge via external information retrieval. In banking—where precision, compliance, and accuracy are vital—optimizing RAG is crucial. This study explores how various document parsing, chunking, and indexing techniques influence the performance of RAG systems in banking contexts. Our evaluation framework measures their effects on retrieval accuracy, contextual relevance, and output quality, offering practical insights for building more reliable and effective RAG solutions.
| Classification | Both methodology and application |
|---|---|
| Keywords | Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Artificial Intelligence (AI), Parsing, Chunking, Indexing, Document Preprocessing, Information Retrieval, Vector Search |
Primary author
MICHAIL MAKRIS
(UNIPI)
Co-authors
Dr
Sotirios Besimis
(UNIPI)
Dr
Vasileios Plagianakos
(University of Thessaly)