Given the growing trend of many organizations integrating Retrieval Augmented Generation (RAG) into their operations, we assess RAG on domain-specific data and test state-of-the-art models across various optimization techniques. We incorporate four optimizations; Multi-Query, Child-Parent-Retriever, Ensemble Retriever, and In-Context-Learning, to enhance the functionality and performance in the academic domain. We focus on data retrieval, specifically targeting various study programs at a large technical university. We additionally introduce a novel evaluation approach, the RAG Confusion Matrix designed to assess the effectiveness of various configurations within the RAG framework. By exploring the integration of both open-source (e.g., Llama2, Mistral) and closed-source (GPT-3.5 and GPT-4) Large Language Models, we offer valuable insights into the application and optimization of RAG frameworks in domain-specific contexts. Our experiments show a significant performance increase when including multi-query in the retrieval phase.

本研究针对在特定领域中整合检索增强生成（RAG）技术的现有不足，提出了四种优化方法以提升其在学术领域的功能和性能。通过引入新的评估方法RAG混淆矩阵，并结合多查询策略，实验结果显示在检索阶段引入多查询显著提高了系统的性能。

在学术数据上优化检索增强生成的方法研究