Abstract

Retrieval-Augmented Generation (RAG) architectures represent a hybrid approach that blends information retrieval with generative modeling to tackle complex natural language processing (NLP) tasks. A key challenge in these systems is optimizing the balance between retrieval depth and generative coherence. Retrieval depth refers to the number of documents retrieved and utilized by the generative model, while generative coherence is the degree to which the generated output is relevant, contextually accurate, and logically consistent with the retrieved information. This paper proposes the RAG Optimization Framework (ROF), designed to fine-tune these factors and enhance performance across diverse applications. We examine various strategies to adjust retrieval depth dynamically, ensuring relevant data retrieval, and we explore techniques to maintain coherence in generative outputs. In addition, this paper investigates how multi-step retrieval can improve performance by progressively refining the information provided to the model. This framework's applications in fields like healthcare and financial document analysis are also discussed, illustrating its potential to significantly enhance RAG systems.

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