The development of large language models (LLMs) has successfully transformed knowledge-based systems such as open domain question nswering, which can automatically produce vast amounts of seemingly coherent information. Yet, those models have several disadvantages like hallucinations or confident generation of incorrect or unverifiable facts. In this paper, we introduce a new approach to the development of expert systems using LLMs in a controlled and transparent way. By limiting the domain and employing a well-structured prompt-based extraction approach, we produce a symbolic representation of knowledge in Prolog, which can be validated and corrected by human experts. This approach also guarantees interpretability, scalability and reliability of the developed expert systems. Via quantitative and qualitative experiments with Claude Sonnet 3.7 and GPT-4.1, we show strong adherence to facts and semantic coherence on our generated knowledge bases. We present a transparent hybrid solution that combines the recall capacity of LLMs with the precision of symbolic systems, thereby laying the foundation for dependable AI applications in sensitive domains.

翻译：大语言模型（LLMs）的发展已成功改造了开放领域问答等知识型系统，这类系统能自动生成海量看似连贯的信息。然而，此类模型存在幻觉、对错误或不可验证事实的自信生成等缺陷。本文提出一种在受控且透明环境下运用LLMs开发专家系统的新方法。通过限定领域并采用基于提示的结构化抽取方法，我们生成Prolog语言中的知识符号表示，该表示可由人类专家验证与修正。此方法同时保障了所开发专家系统的可解释性、可扩展性与可靠性。通过对Claude Sonnet 3.7和GPT-4.1的定量与定性实验，我们在生成的知识库上展现出对事实的强遵循性与语义连贯性。我们提出了一种透明混合解决方案，将LLMs的检索能力与符号系统的精确性相结合，从而为敏感领域可信赖的人工智能应用奠定基础。