ARAGOG: Comprehensive Evaluation of Advanced Retrieval-Augmented Generation Techniques

This study provides a comprehensive evaluation of advanced Retrieval-Augmented Generation (RAG) techniques, focusing on their impact on retrieval precision and answer similarity. The findings highlight the strengths and weaknesses of various RAG approaches, including Sentence Window Retrieval, Hypothetical Document Embedding (HyDE), and LLM-based reranking, offering insights to guide the development and application of effective RAG systems.