Why Financial RAG Needs More Than Better Embeddings

I ended the last article (Linked: Part 2) on a single root cause, structured, quantitative data forced through a pipe built for unstructured language, producing the linearization defect during extraction and the tokenization disconnect during embedding. The natural conclusion is that you need a better extractor.

That conclusion is half right, and the missing half is what this article is about. Flattening is the default at every step along the way, because every component in a retrieval-augmented generation pipeline expects the one before it to hand over plain running text, with nothing marking which part was a header and which was a figure. The parser turns a two-dimensional page into a one-dimensional line, the embedding model reduces a whole table to one location in a vector space, and the retriever treats every question, conceptual or arithmetic, as the same nearest neighbour search. That is the flattening reflex, and fixing...

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