Towards Data Science·
Enterprise Document Intelligence [Vol. 1 #2bis] Why stacking a reranker on top of weak retrieval doesn’t save it, what cross-encoders actually fix vs what they don’t, and where the editorial position of the series lands. The post Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost appeared first on Towards Data Science.
Read full articleEnterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does. The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval appeared first on Towards Data Science.
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. The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science.
In this tutorial, we use zeroentropy/zerank-2-reranker, a 4B Qwen3-based cross-encoder reranker, to improve retrieval quality. We start by setting up the runtime, loading the reranker, and understanding how it scores query-document pairs. Then, we move from simple pairwise scoring to a practical two-stage retrieve-and-rerank pipeline, where a fast bi-encoder first retrieves candidates and zerank-2 reranks […] The post Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker appeared first on MarkTechPost.
For AI engineers who want to understand every step, not just call the library The post Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale appeared first on Towards Data Science.
