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Enterprise Document Intelligence [Vol.1 #M3] – The ten positions the series argues from, and the…
24 min read -
Enterprise Document Intelligence [Vol.1 #7sexies] – The unit of retrieval doesn’t have to be a…
15 min read -
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
Large Language ModelsEnterprise Document Intelligence [Vol.1 #8quater] – Two angles on the cascade, cost and a validation…
13 min read -
Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract
Large Language ModelEnterprise Document Intelligence [Vol.1 #8ter] – Naming the RAG error correctly matters: model reads the…
11 min read -
Enterprise Document Intelligence [Vol.1 #8bis] – Two regimes for sending retrieved candidates to the generation…
9 min read -
Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Large Language ModelEnterprise Document Intelligence [Vol.1 #6quinquies] – Prompt engineering, then context engineering, then loop engineering. On…
13 min read -
Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
Large Language ModelEnterprise Document Intelligence [Vol.1 #6quater] – Question parsing takes one messy string and writes four…
16 min read -
Enterprise Document Intelligence [Vol.1 #7quinquies] – Hallucination is usually garbage-in. Fix retrieval, and the model…
13 min read -
Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents
Large Language ModelEnterprise Document Intelligence [Vol.1 #7quater] – A 492-page document has a 358-entry table of contents.…
10 min read -
Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop
Large Language ModelEnterprise Document Intelligence [Vol.1 #8C] – Structured output is the start of validation, not the…
26 min read
