Paper Contents
Abstract
Large Language Models (LLMs) have revolutionized natural language processing (NLP) by demonstrating remarkable capabilities in text generation and understanding. However, their responses are often limited by the knowledge available during pretraining, leading to outdated or incomplete information. Retrieval-Augmented Generation (RAG) is an emerging technique that enhances LLM performance by incorporating external knowledge from vector databases during the generation process. This paper explores the architecture, methodologies, and practical applications of RAG, highlighting its potential in improving response accuracy, reducing hallucination, and enhancing contextual relevance. We also discuss the implementation of RAG pipelines, the role of vector databases in efficient semantic search, and the challenges of retrieval efficiency, data freshness, and handling noisy retrievals. Finally, we present experimental results demonstrating the effectiveness of RAG in domain-specific applications, such as customer support, medical information retrieval, and legal document analysis.
Copyright
Copyright © 2025 Bilal Ikbal Kureshi. This is an open access article distributed under the Creative Commons Attribution License.
