{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"Why Financial RAG Needs More Than Better Embeddings","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/d248f253\"></iframe>","width":"100%","height":180,"duration":1624,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/why-financial-rag-needs-more-than-better-embeddings.\nWhy RAG fails on financial documents even with perfect retrieval, and three architectural fixes: layout-aware parsing, multi-vector retrieval, and routing math.\nCheck more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #rag, #rag-architecture, #rag-pipelines, #rag-optimization, #rag-implementation, #hybrid-rag, #rag-pipeline, #rag-evaluation,  and more.\nThis story was written by: @shrirams. Learn more about this writer by checking @shrirams's about page,\n            and for more stories, please visit hackernoon.com.\nEven 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.","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}