Large language models show promise for knowledge-intensive domains, yet their use in agriculture is constrained by weak grounding, English-centric training data, and limited real-world evaluation. These issues are amplified for low-resource languages, where high-quality domain documentation exists but remains difficult to access through general-purpose models. This paper presents AgriHubi, a domain-adapted retrieval-augmented generation (RAG) system for Finnish-language agricultural decision support. AgriHubi integrates Finnish agricultural documents with open PORO family models and combines explicit source grounding with user feedback to support iterative refinement. Developed over eight iterations and evaluated through two user studies, the system shows clear gains in answer completeness, linguistic accuracy, and perceived reliability. The results also reveal practical trade-offs between response quality and latency when deploying larger models. This study provides empirical guidance for designing and evaluating domain-specific RAG systems in low-resource language settings.

翻译：大型语言模型在知识密集型领域展现出潜力，但其在农业领域的应用受限于薄弱的基础支撑、以英语为中心的训练数据以及有限的实际评估。这些问题在低资源语言环境中更为突出，尽管存在高质量的领域文档，但通过通用模型仍难以有效访问。本文提出AgriHubi，一个面向芬兰语农业决策支持的领域自适应检索增强生成（RAG）系统。AgriHubi将芬兰语农业文档与开源PORO系列模型相结合，并通过显式来源追溯与用户反馈机制支持迭代优化。经过八次迭代开发并通过两项用户研究评估，该系统在答案完整性、语言准确性和感知可靠性方面均显示出显著提升。研究结果同时揭示了部署较大模型时响应质量与延迟之间的实际权衡。本研究为低资源语言环境下领域特定RAG系统的设计与评估提供了实证指导。