Enterprise Document Intelligence [Vol. 1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs The post RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream call The post Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation appeared first on Towards Data Science .
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #6b] - The five field families the parser reads straight from the user’s question, with the code that fills each one The post What the Question Parser Extracts from a User String: Keywords, Scope, Shape, Decomposition, Clarification appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
By Kezhan 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 #9A] - Same paper, same question as Article 1.
By angela shi