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 trade-offs. 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.