OPTIMIZING RAG SYSTEMS WITH FINE-TUNING TECHNIQUES - Tureniyazova, Asiya Ibragimovna; Sprishevskiy, Kirill Vladimirovich (Educational Research in Universal Sciences, 2024)
This study explores the optimization of Retrieval-Augmented Generation (RAG) systems through fine-tuning techniques in natural language processing. Results demonstrate significant improvements in accuracy, relevance, and efficiency. Fine-tuning enhances RAG models' adaptability to specific tasks or domains, paving the way for transformative advancements in information retrieval and content generation.