Enterprise Document Intelligence [Vol. 1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering.
By Kezhan Shi
Enterprise Document Intelligence [Vol. 1 #13bis] - The four bricks return useful results most of the time.
By angela shi
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
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 #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 #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