Enterprise Document Intelligence [Vol. 1 #13bis] - The four bricks return useful results most of the time.
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
Enterprise Document Intelligence [Vol. 1 #9ter] - The pipeline from Article 9 calls a model at several steps to be sure it is right.
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
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.
By Emmimal P Alexander
Why use it? How to implement it?
By Shuai Guo