Enhancing Retrieval-Augmented Generation: A Study of Best Practices

TL;DR

This paper systematically investigates various components and configurations of Retrieval-Augmented Generation systems, proposing advanced designs and providing insights to optimize their performance across diverse applications.

Contribution

It introduces novel RAG system designs including query expansion, new retrieval strategies, and Contrastive In-Context Learning, with comprehensive analysis of factors affecting response quality.

Findings

Key factors like model size and prompt design significantly impact performance.

Contrastive In-Context Learning enhances retrieval relevance.

Optimal configurations balance contextual richness and efficiency.

Abstract

Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. However, the influence of various components and configurations within RAG systems remains underexplored. A comprehensive understanding of these elements is essential for tailoring RAG systems to complex retrieval tasks and ensuring optimal performance across diverse applications. In this paper, we develop several advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. Our study systematically investigates key factors, including language model size, prompt design, document chunk size, knowledge base size, retrieval stride, query expansion techniques, Contrastive In-Context Learning knowledge bases, multilingual knowledge bases, and Focus Mode retrieving relevant context at sentence-level. Through extensive experimentation, we provide a detailed analysis of how these factors influence response quality. Our findings offer actionable insights for developing RAG systems, striking a balance between contextual richness and retrieval-generation efficiency, thereby paving the way for more adaptable and high-performing RAG frameworks in diverse real-world scenarios. Our code and implementation details are publicly available.