Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .
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 .
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 .
Enterprise Document Intelligence [Vol. 1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call The post Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs appeared first on Towards Data Science .
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 .
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 .
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 .
Enterprise Document Intelligence [Vol. 1 #5nonies] - Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus The post Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #7C] - One LLM call ranks the candidates with reasons.
Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.
Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.
Enterprise Document Intelligence [Vol. 1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025.
Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.