The article "How Does a RAG Reranker Really Work?" explores the inner workings of Retrieval-Augmented Generation (RAG) rerankers, focusing on how data scientists explain the model’s operations behind the scenes. It discusses the impact of these insights on architecture decisions within enterprise document intelligence, specifically in the context of Enterprise Document Intelligence Vol.1 #2D. The piece highlights the importance of transparent model explanations for effective enterprise RAG implementation.
By Kezhan Shi
The article explains how to handle problems that arise when creating a new DAX measure based on an existing one while attempting to overwrite a filter that the nested measure already applies. It discusses the common scenario of reusing measures and the complications that can occur when the same filter is modified in the outer measure. The post provides guidance on resolving these issues to ensure correct calculation results.
By Salvatore Cagliari
Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
By angela shi
arXiv:2609.12835v1 Announce Type: new
Abstract: Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories...
By Afshin Bozorgpour, Peter Sch\"uffler, Edgar Jost, Dorit Merhof
For years, I created date tables with DAX code whenever I didn’t have a way to create them upstream of the data flow. Now I've realised there's another way to do it.
By Salvatore Cagliari
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi
Having categorized data is everything in reporting. Uncategorized data cannot be grouped and aggregated.
By Salvatore Cagliari
Towards Data Science has announced a major overhaul of its website and contributor portal. The new site promises faster performance and a brand‑new portal for writers, aiming to improve the experience for both readers and contributors. The update is positioned as a significant upgrade for anyone who reads, writes, or both on the platform.
By TDS Editors
The article outlines a framework for constructing Retrieval-Augmented Generation (RAG) pipelines that progressively add complexity as needed to address observed failure modes. It begins with basic lexical and hybrid search techniques, then incorporates reranking and agentic information‑seeking strategies to improve performance. The approach emphasizes that more sophisticated components should only be introduced when simpler methods prove insufficient.
By Tahreem Rasul
Enterprise Document Intelligence [Vol. 1 #M1] - The thesis behind every architectural choice in this series The post Amplify the Expert: A Philosophy for Building Enterprise RAG appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted.
By angela shi