Why Financial RAG Needs More Than Better Embeddings

This story was originally published on HackerNoon at: https://hackernoon.com/why-financial-rag-needs-more-than-better-embeddings. Why RAG fails on financial documents even with perfect retrieval, and three architectural fixes: layout-aware parsing, multi-vector retrieval, and routing math. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #rag, #rag-architecture, #rag-pipelines, #rag-optimization, #rag-implementation, #hybrid-rag, #rag-pipeline, #rag-evaluation, and more. This story was written by: @shrirams. Learn more about this writer by checking @shrirams's about page, and for more stories, please visit hackernoon.com. Even with perfect retrieval, a leading model gets 15% of financial questions wrong. In a realistic setup, that number hits 81%. The reason is structural, every stage of a standard RAG pipeline flattens the table, page, or question it's handed. Three fixes, one per seam.