Enhancing language models with retrieval-augmented generation

Abstract

This paper explores the core principles of RAG, its benefits — such as improved factual accuracy and reduced hallucinations — and its challenges, including computational overhead and data reliability concerns. Additionally, real-world applications and recent research in RAG-based systems are reviewed.