Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory

ArXi:2603.03294v2 Announce Type: replace-cross Large Language Models show promise for agricultural advisory, yet vanilla models exhibit uned recommendations, generic advice lacking specific, actionable detail, and communication styles misaligned with smallholder farmer needs. In high stakes agricultural contexts, where recommendation accuracy has direct consequences for farmer outcomes, these limitations pose challenges for responsible deployment.