Show HN: RAG chunk size "best practices" failed on legal text – I benchmarked it (medium.com)

A recent exploration of retrieval-augmented generation (RAG) practices has revealed that conventional wisdom about chunk sizes can lead to significant declines in performance, especially in specialized fields like legal text. Typically, best practices suggest using 512-character chunks for document segmentation, but a new benchmarking tool called RagTune, developed by an AI researcher, demonstrated that applying this rule can result in a 7% drop in recall for legal documents. This misstep can transform an AI assistant from helpful to ineffective, underscoring the critical need for tailored approaches in different contexts. RagTune efficiently evaluates RAG retrieval systems by measuring metrics such as Recall@K and Mean Reciprocal Rank across various datasets. In tests across general knowledge and legal texts, only general content showed negligible differences across chunk sizes, while legal text performance plummeted from 99% to 66% recall when larger chunks were used. The tool emphasizes the intricacies of legal language, which demands precise context that larger embeddings struggle to capture. This finding underscores a vital takeaway for AI developers: relying on generic settings is insufficient; measuring and customizing retrieval layers are essential for delivering accurate and reliable AI outputs in specialized domains.