A Novel AI System for Trustworthy and Accurate Diagnostics and Troubleshooting

The emergence of large-language models (LLMs) in everyday applications has highlighted their capability to provide human-like dialog and answer complex questions. However, LLMs often exhibit a tendency to "hallucinate," generating responses that appear factual but are incorrect, even when trained on ground truth data. In high-stakes applications such as medical diagnostics and mechanical troubleshooting, the need for accurate, trustworthy, and explainable AI systems is paramount. Without these qualities, such systems cannot be fully relied upon for critical decision-making. In this paper, we propose a novel AI system designed to address these challenges. The system integrates an AI agent, a logic model, an LLM, and a prompt engine to enhance accuracy and trustworthiness in a sequence of dialog. Experimental results demonstrate that the proposed system achieves an average accuracy of 86.49% across three diagnostic and troubleshooting tasks in healthcare, automotive maintenance, and digital equipment applications. In contrast, the baseline LLM only approach achieves an average accuracy of 63.42%, representing an absolute accuracy improvement of 23.07% with our approach. Additionally, the proposed system achieves a 4.26-fold speedup in response time across the experiments. These results highlight the potential of the proposed system to enhance reliability and efficiency in critical real-world applications.