Retrieval-Augmented Generation for Industrial Applications: Design and Evaluation of an AI Assistant
Abstract
Industrial machinery manufacturers distribute technical knowledge through dense manuals that operators and engineers must navigate manually to answer operational questions.This process is time-consuming, prone to error, and inaccessible to users without prior familiarity with the documentation.Large language models offer a promising alternative, but their reliance on static parametric knowledge makes them unsuitable for domain-specific technical queries where safety, accuracy and grounding in authoritative sources are critical.This dissertation presents AMOB-RAG, a modular Retrieval-Augmented Generation system designed to answer natural language questions in Portuguese about technical documentation for rotary draw bending machines.The system is structured around two pipelines: an offline ingestion pipeline that preprocesses, chunks, and indexes markdown documentation into a persistent vector store, and an online query pipeline that retrieves, reranks, and assembles context before generating grounded answers.Key design decisions include semantic chunking with configurable breakpoint detection, CrossEncoder reranking with a table penalty heuristic, domain-specific query expansion from a structured axis synonym file, and a similarity threshold gate that prevents low-confidence retrievals from reaching the generation stage.The system is evaluated against a human-verified golden dataset of 20 question-answer pairs in Portuguese, covering five question categories.Evaluation…
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