This paper introduces a new hyper-parameter for Retrieval-Augmented Generation (RAG) systems called Context Window Utilization. RAG systems enhance generative models by incorporating relevant information retrieved from external knowledge bases, improving the factual accuracy and contextual relevance of generated responses. The size of the text chunks retrieved and processed is a critical factor influencing RAG performance. This study aims to identify the optimal chunk size that maximizes answer generation quality. Through systematic experimentation, we analyze the effects of varying chunk sizes on the efficiency and effectiveness of RAG frameworks. Our findings reveal that an optimal chunk size balances the trade-off between providing sufficient context and minimizing irrelevant information. These insights are crucial for enhancing the design and implementation of RAG systems, underscoring the importance of selecting an appropriate chunk size to achieve superior performance.

本研究解决了检索增强生成（RAG）系统中上下文窗口大小对生成质量的影响这一问题。通过系统实验，我们提出了一个新的超参数——上下文窗口利用，以优化文本块大小，平衡上下文信息的充分性与无关信息的最小化。研究结果表明，选择合适的文本块大小可以显著提升RAG系统的性能。

引入RAG的新超参数：上下文窗口利用