We study how document chunking choices impact the reliability of Retrieval-Augmented Generation (RAG) systems in industry. While practice often relies on heuristics, our end-to-end evaluation on Natural Questions systematically varies chunking method (token, sentence, semantic, code), chunk size, overlap, and context length. We use a standard industrial setup: SPLADE retrieval and a Mistral-8B generator. We derive actionable lessons for cost-efficient deployment: (i) overlap provides no measurable benefit and increases indexing cost; (ii) sentence chunking is the most cost-effective method, matching semantic chunking up to ~5k tokens; (iii) a "context cliff" reduces quality beyond ~2.5k tokens; and (iv) optimal context depends on the goal (semantic quality peaks at small contexts; exact match at larger ones).

翻译：本研究探讨了文档分块选择如何影响工业界检索增强生成（RAG）系统的可靠性。尽管实践中通常依赖启发式方法，但我们在Natural Questions数据集上进行了端到端评估，系统性地改变了分块方法（基于词元、句子、语义、代码）、分块大小、重叠度以及上下文长度。我们采用标准的工业设置：SPLADE检索器和Mistral-8B生成器。我们得出了具有成本效益部署的可操作经验：（i）重叠未带来可测量的益处，反而增加了索引成本；（ii）句子分块是最具成本效益的方法，在约5k词元内其效果与语义分块相当；（iii）超过约2.5k词元会出现“上下文悬崖”，导致质量下降；以及（iv）最优上下文取决于具体目标（语义质量在小上下文中达到峰值，而精确匹配则需要更大的上下文）。