Analysis on the Applications of Retrieval Augmented Generation (RAG) Architecture
Analysis on the Applications of Retrieval Augmented Generation (RAG) Architecture

Author(s): Bozhidar Bahov
Subject(s): Social Sciences, Economy, Business Economy / Management, Sociology, Evaluation research, ICT Information and Communications Technologies
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: Retrieval-Augmented Generation; Large Language Models; Vector Databases; Information Retrieval; Natural Language Processing
Summary/Abstract: This paper explores the applications and implications of Retrieval-Augmented Generation (RAG) architectures in various business domains, highlighting their ability to overcome the limitations of traditional Large Language Models (LLMs). Unlike fine-tuning, which demands significant computational resources and struggles with domain-specific nuances, RAG efficiently integrates external knowledge to deliver accurate and contextually relevant responses. The paper discusses the core components of RAG systems, including vector databases and retrieval models, and showcases their practical and business.

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