{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/23417"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/23417","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"AI Retrieval-Augmented Generation (RAG) System with Engineering Document Information Extraction for Employee Development","abstract":"This paper explores the implementation of AI large language model (LLM) support systems that utilize retrieval-augmented generation (RAG) to enhance employee effectiveness in trouble-shooting on a manufacturing production line. The RAG-augmented LLM tool is designed to ex-tract relevant data from proprietary engineering documents, including engineering specifications, product engineering documents, troubleshooting tool manuals, and manufacturing logging data, and convey this information in a comprehensible format for troubleshooting purposes. The antici-pated effects of training with the tool include improvements in employee technical communica-tion, product and process technical literacy, and the timeliness and accuracy of investigations. Increasing the performance of technical employees helps businesses meet customer deadlines and quality expectations and decreases the need for expert support. The advantage of this system over a collection of documents and data is that anyone can use it without extensive familiarity with the engineering documents, allowing a wider range of employees to learn to navigate them and raise alarms about potential systemic issues.","abstract_html":"This paper explores the implementation of AI large language model (LLM) support systems that utilize retrieval-augmented generation (RAG) to enhance employee effectiveness in trouble-shooting on a manufacturing production line. The RAG-augmented LLM tool is designed to ex-tract relevant data from proprietary engineering documents, including engineering specifications, product engineering documents, troubleshooting tool manuals, and manufacturing logging data, and convey this information in a comprehensible format for troubleshooting purposes. The antici-pated effects of training with the tool include improvements in employee technical communica-tion, product and process technical literacy, and the timeliness and accuracy of investigations. Increasing the performance of technical employees helps businesses meet customer deadlines and quality expectations and decreases the need for expert support. The advantage of this system over a collection of documents and data is that anyone can use it without extensive familiarity with the engineering documents, allowing a wider range of employees to learn to navigate them and raise alarms about potential systemic issues.","abstract_has_math":false,"creators":["Perry, Robert"],"institution":"Texas State University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Aslan, Semih"],"committee_chairs":[],"committee_members":["Valles, Damian","Dutta, Anandi K."],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-27T21:22:34Z","subjects":["AI","RAG","documents","artificial intelligence","retrieval augmented generation","documentation","employees"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10877/23417","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Aslan, Semih"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Valles, Damian","Dutta, Anandi K."]},{"key":"dc:creator","label":"Author","values":["Perry, Robert"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-03T17:37:08Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Texas State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AI","RAG","documents","artificial intelligence","retrieval augmented generation","documentation","employees"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10877/23417"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This paper explores the implementation of AI large language model (LLM) support systems that utilize retrieval-augmented generation (RAG) to enhance employee effectiveness in trouble-shooting on a manufacturing production line. The RAG-augmented LLM tool is designed to ex-tract relevant data from proprietary engineering documents, including engineering specifications, product engineering documents, troubleshooting tool manuals, and manufacturing logging data, and convey this information in a comprehensible format for troubleshooting purposes. The antici-pated effects of training with the tool include improvements in employee technical communica-tion, product and process technical literacy, and the timeliness and accuracy of investigations. Increasing the performance of technical employees helps businesses meet customer deadlines and quality expectations and decreases the need for expert support. 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The RAG-augmented LLM tool is designed to ex-tract relevant data from proprietary engineering documents, including engineering specifications, product engineering documents, troubleshooting tool manuals, and manufacturing logging data, and convey this information in a comprehensible format for troubleshooting purposes. The antici-pated effects of training with the tool include improvements in employee technical communica-tion, product and process technical literacy, and the timeliness and accuracy of investigations. Increasing the performance of technical employees helps businesses meet customer deadlines and quality expectations and decreases the need for expert support. 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