Enterprise Document Intelligence [Vol. 1 #7quater] - A 492-page document has a 358-entry table of contents.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.
Enterprise Document Intelligence [Vol. 1 #8bis] - Two regimes for sending retrieved candidates to the generation brick, the sufficiency signal that picks between them, and the per-question type dispatch that makes it cheap The post Loop Engineering for RAG Generation: Iterate top-k One at a Time appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Finding the right anchors for RAG: keyword, embedding, and TOC signals in parallel appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context The post Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.
The article discusses a method for handling a folder of unrelated PDFs as a single long document with a nested outline. It highlights that without shared fields, an index cannot be built, so the approach uses one summary line per file and each file’s own table of contents, with retrieval routes extending down two levels. This structure enables retrieval-augmented generation (RAG) across multiple documents.