Our project is to develop a conversational assistant to aid users in understanding and choosing appropriate agricultural insurance policies. The assistant leverages a Large Language Model (LLM) trained on datasets from the Rainfall Index Insurance Standards Handbook and USDA site information. It is designed to provide clear, easily understood explanations and guidance, helping users navigate their insurance options. The project encompasses the development of an accessible chat interface, backend integration with a Flask API, and the deployment of the assistant on Virginia Tech's Endeavour cluster. Through personalized recommendations and visualizations, the assistant empowers users to make well-informed decisions regarding their insurance needs. Our project report and presentation outline the project's objectives, design, implementation, and lessons learned, highlighting the potential impact of this interactive conversational assistant in simplifying the complex process of selecting agricultural insurance policies.


A web application to provide recommendations and answers to questions related to agricultural insurance using Large Language Models. The final report is available in Word and PDF versions in files AgricultureInsuranceLLMsReport.docx and AgricultureInsuranceLLMsReport.pdf. The final presentation is available in PowerPoint and PDF versions in files AgricultureInsuranceLLMsPresentation.pptx and AgricultureInsuranceLLMsPresentation.pdf. License: GNU General Public License v3.0,


agriculture, insurance, drought, LLMs, RAG