In this technical data systems study, YantriX investigated a retrieval-augmented generation (RAG) architecture tailored for engineering workflows—evaluating hybrid semantic search, chunking strategies for technical standards, and grounded citation verification across 40,000 document segments.
Overview
Software architecture study developing a retrieval-augmented generation (RAG) system with hybrid search and citation tracking across engineering drawings and CAD documentation.
Discipline: Mechanical & CAD
Project Type: Technical Data Engineering (RAG Architecture & Retrieval Study)
Application: Engineering Knowledge Management & CAD Documentation
YantriX Scope Ownership
Key specifications
Objectives
Challenge
Engineering teams lose hours searching through legacy datasheets, fabrication standards, and CAD drawing archives. Generic generative AI tools frequently hallucinate numerical tolerances and material grades, making ungrounded answers dangerous in physical engineering. This study evaluated how to build an engineering-grade retrieval system where every response cites specific drawing paragraphs and tables.
Approach
Engineered layout-aware PDF extraction isolating tables, drawing title blocks, and notes.
Implemented a two-stage retrieval pipeline: initial hybrid retrieval (dense + BM25) followed by a cross-encoder reranker.
Constrained language model response generation to only use information from retrieved context passages.
Evaluated retrieval faithfulness and answer accuracy using the RAGAS evaluation harness.
Outcomes
Deliverables
Tools used
Impact
Conclusion
Engineering documentation systems require strict grounding. By combining layout-aware chunking, hybrid search, and citation verification, teams can locate technical standards quickly and reliably.
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Tagged
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