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 #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 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
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
The article discusses how an FAQ can invert the traditional Retrieval-Augmented Generation (RAG) pipeline. It explains that parsing becomes trivial, retrieval functions as a cache, and few‑shot prompting is reframed as a retrieval problem. The piece highlights the practical implications of designing a corpus around an FAQ structure for enterprise document intelligence.
By Kezhan Shi
The article argues that Retrieval-Augmented Generation (RAG) is only one tool in NLP, and many real-world problems—such as request classification, free‑text matching, table reading, and OCR noise cleaning—are better served by simpler, cheaper techniques. It emphasizes the importance of selecting the appropriate method for each task and highlights the engineering challenge of knowing which technique to apply.
By Kezhan Shi