Challenged by data-driven AI limitations in reasoning and knowledge depth, this work presents a novel approach for enhanced conversational understanding. We leverage advanced text analysis to strategically extract key information from FAQs, then utilize AI-generated questions and robust semantic similarity metrics to significantly improve user query matching precision. Through the strategic integration of important sentence extraction in knowledge preparation, coupled with question generation and the application of semantic textual similarity measures, our model achieves a substantial improvement in user query matching precision. We propose a dual-system architecture—augmenting System 1 with additional knowledge akin to System 2 in human cognition. The methodology is exemplified through chatbot correction using FAQs, demonstrating the potential for human-like mind processing. Results showcase improved semantic understanding and reasoning, offering a promising path for advancing AI capabilities in conversational contexts.