  Chunking information is a key step in Retrieval Augmented Generation (RAG).
Current research primarily centers on paragraph-level chunking. This approach
treats all texts as equal and neglects the information contained in the
structure of documents. We propose an expanded approach to chunk documents by
moving beyond mere paragraph-level chunking to chunk primary by structural
element components of documents. Dissecting documents into these constituent
elements creates a new way to chunk documents that yields the best chunk size
without tuning. We introduce a novel framework that evaluates how chunking
based on element types annotated by document understanding models contributes
to the overall context and accuracy of the information retrieved. We also
demonstrate how this approach impacts RAG assisted Question & Answer task
performance. Our research includes a comprehensive analysis of various element
types, their role in effective information retrieval, and the impact they have
on the quality of RAG outputs. Findings support that element type based
chunking largely improve RAG results on financial reporting. Through this
research, we are also able to answer how to uncover highly accurate RAG.
