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Authors: Pedro Bilar Montero ; Jonas Bulegon Gassen ; Glênio Descovi ; Vinícius Maran ; Tais Oltramari Barnasque ; Matheus Friedhein Flores and Alencar Machado

Affiliation: Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC), Federal University of Santa Maria, Brazil

Keyword(s): Retrieval-Augmented Generation, Large Language Models, Poultry Health, Sanitary Certification, PDSA-RS, Brazilian Regulations, Legal Texts, Animal Health, Natural Language Processing.

Abstract: This study explores the potential of integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to enhance the accuracy and relevance of responses in domain-specific tasks, particularly within the context of animal health regulation. Our proposal solution incorporates a RAG system on the PDSA-RS platform, leveraging an external knowledge base to integrate localized legal information from Brazilian legislation into the model’s response generation process. By combining LLMs with an information retrieval module, we aim to provide accurate, up-to-date responses grounded in relevant legal texts for professionals in the veterinary health sector.

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Paper citation in several formats:
Montero, P. B., Gassen, J. B., Descovi, G., Maran, V., Barnasque, T. O., Flores, M. F. and Machado, A. (2025). Improving Large Language Models Responses with Retrieval Augmented Generation in Animal Production Certification Platforms. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8; ISSN 2184-4992, SciTePress, pages 489-500. DOI: 10.5220/0013286100003929

@conference{iceis25,
author={Pedro Bilar Montero and Jonas Bulegon Gassen and Glênio Descovi and Vinícius Maran and Tais Oltramari Barnasque and Matheus Friedhein Flores and Alencar Machado},
title={Improving Large Language Models Responses with Retrieval Augmented Generation in Animal Production Certification Platforms},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={489-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013286100003929},
isbn={978-989-758-749-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Improving Large Language Models Responses with Retrieval Augmented Generation in Animal Production Certification Platforms
SN - 978-989-758-749-8
IS - 2184-4992
AU - Montero, P.
AU - Gassen, J.
AU - Descovi, G.
AU - Maran, V.
AU - Barnasque, T.
AU - Flores, M.
AU - Machado, A.
PY - 2025
SP - 489
EP - 500
DO - 10.5220/0013286100003929
PB - SciTePress

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