BeefBot: Harnessing Advanced LLM and RAG Techniques for Providing Scientific and Technology Solutions to Beef Producers
Zhang, Zhihao, Wilson, Carrie-Ann, Hay, Rachel, Everingham, Yvette, and Naseem, Usman (2025) BeefBot: Harnessing Advanced LLM and RAG Techniques for Providing Scientific and Technology Solutions to Beef Producers. In: Proceedings of the 31st International Conference on Computational Linguistics. pp. 54-62. From: COLING 2025: 31st International Conference on Computational Linguistics, 19-24 January 2025, Abu Dhabi, United Arab Emirates.
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Abstract
We propose BeefBot, a LLM-powered chatbot designed for beef producers. It retrieves the latest agricultural technologies (AgTech), practices and scientific insights to provide rapid, domain-specific advice, helping to address on-farm challenges effectively. While generic Large Language Models (LLMs) like ChatGPT are useful for information retrieval, they often hallucinate and fall short in delivering tailored solutions to the specific needs of beef producers, including breed-specific strategies, operational practices, and regional adaptations.There are two common methods for incorporating domain-specific data in LLM applications: Retrieval-Augmented Generation (RAG) and fine-tuning. However, their respective advantages and disadvantages are not well understood. Therefore, we implement a pipeline to apply RAG and fine-tuning using an open-source LLM in BeefBot and evaluate the tradeoffs. By doing so, we are able to select the best combination as the backend of BeefBot, delivering actionable recommendations that enhance productivity and sustainability for beef producers with fewer hallucinations. Key benefits of BeefBot include its accessibility as a web-based platform compatible with any browser, continuously updated knowledge through RAG, confidential assurance via local deployment, and a user-friendly experience facilitated by an interactive website. The demo of the BeefBot can be accessed at https://www.youtube.com/watch?v=r7mde1EOG4o.
| Item ID: | 88418 |
|---|---|
| Item Type: | Conference Item (Research - E1) |
| ISBN: | 9798891761988 |
| ISSN: | 2951-2093 |
| Copyright Information: | © 2025 Association for Computational Linguistics. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. |
| Date Deposited: | 19 Feb 2026 00:34 |
| FoR Codes: | 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3003 Animal production > 300399 Animal production not elsewhere classified @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 50% |
| SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% 10 ANIMAL PRODUCTION AND ANIMAL PRIMARY PRODUCTS > 1004 Livestock raising > 100401 Beef cattle @ 50% |
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