RAG has become a key technique for enhancing LLMs by reducing hallucinations, especially in domain expert systems where LLMs may lack sufficient inherent knowledge. However, developing these systems in low-resource settings introduces several challenges: (1) handling heterogeneous data sources, (2) optimizing retrieval phase for trustworthy answers, and (3) evaluating generated answers across diverse aspects. To address these, we introduce a data generation pipeline that transforms raw multi-modal data into structured corpus and Q&A pairs, an advanced re-ranking phase improving retrieval precision, and a reference matching algorithm enhancing answer traceability. Applied to the automotive engineering domain, our system improves factual correctness (+1.94), informativeness (+1.16), and helpfulness (+1.67) over a non-RAG baseline, based on a 1-5 scale by an LLM judge. These results highlight the effectiveness of our approach across distinct aspects, with strong answer grounding and transparency.

翻译：检索增强生成（RAG）已成为通过减少幻觉来增强大语言模型（LLM）的关键技术，尤其在LLM可能缺乏足够内在知识的领域专家系统中。然而，在低资源环境下开发此类系统面临若干挑战：（1）处理异构数据源，（2）为获得可信答案优化检索阶段，以及（3）从多个维度评估生成的答案。为此，我们引入了一个数据生成流水线，将原始多模态数据转化为结构化语料库和问答对；一个提升检索精度的进阶重排序阶段；以及一个增强答案可追溯性的参考匹配算法。在汽车工程领域的应用表明，基于LLM评判员1-5分的评分标准，我们的系统相较于非RAG基线在事实正确性（+1.94）、信息丰富度（+1.16）和实用性（+1.67）方面均有提升。这些结果凸显了我们的方法在不同维度上的有效性，并具备强大的答案依据性和透明度。