A production RAG pipeline for real-world PDFs: structural retrieval, typed answers, cited lines
Last Updated on July 6, 2026 by Editorial Team
Author(s): Angela Shi
Originally published on Towards AI.
The four bricks, run end-to-end on a real 45-page car-insurance policy. One surprising coverage question, answered with a number and the exact line it came from
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After the lead, the article walks through a production-ready RAG approach built as four end-to-end “bricks” rather than a naive embedding-and-generation baseline: it parses real PDFs into relational tables (lines, bounding boxes, pages, and a reconstructed table of contents), turns the user question into a typed brief that includes intent, expected answer shape (e.g., capped dollar amount), section hints, and expert vocabulary, routes retrieval by section filtering using an anchor-and-router pattern to avoid confusing similarly embedded caps, and finally generates a constrained “contract” output (typed value, evidence span with exact line coordinates, confidence, and caveats). It concludes by showing how the same typed pipeline scales to harder question types like listings, decompositions, and scoped synthesis, emphasizing that auditable intermediate objects and a consistent contract make it reliable for real-world document QA.
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