The hardest part in RAQ is document parsing. If you only consider text then it should be ok, but once you start having tables, tables going multiple pages, charts, ignore TOC when available, footnotes … etc, that part becomes really hard and accuracy suffers to get the context regardless of what chunking do you use.

There are some patterns to help such as RAPTOR where you make ingestion content aware and instead of just ingesting content, you start using LLMs to question and summarise the content and save that to the vector database.

But reality is, having one size fits all for RAQ is not an easy task.