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 .
By angela shi
Enterprise Document Intelligence [Vol. 1 #7A] - Stop searching strings.
By angela shi
Enterprise Document Intelligence [Vol. 1 #7quater] - A 492-page document has a 358-entry table of contents.
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #7C] - One LLM call ranks the candidates with reasons.
By angela shi
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
Enterprise 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.
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 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG The post The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation appeared first on Towards Data Science .
By Kezhan Shi
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 .
By angela shi
Enterprise Document Intelligence [Vol. 1 #9A] - Same paper, same question as Article 1.
By angela shi
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.
By angela shi
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 .
By Kezhan Shi