  Smart cities need the involvement of their residents to enhance quality of
life. Conversational query-answering is an emerging approach for user
engagement. There is an increasing demand of an advanced conversational
question-answering that goes beyond classic systems. Existing approaches have
shown that LLMs offer promising capabilities for CQA, but may struggle to
capture the nuances of conversational contexts. The new approach involves
understanding the content and engaging in a multi-step conversation with the
user to fulfill their needs. This paper presents a novel method to elevate the
performance of Persian Conversational question-answering (CQA) systems. It
combines the strengths of Large Language Models (LLMs) with contextual keyword
extraction. Our method extracts keywords specific to the conversational flow,
providing the LLM with additional context to understand the user's intent and
generate more relevant and coherent responses. We evaluated the effectiveness
of this combined approach through various metrics, demonstrating significant
improvements in CQA performance compared to an LLM-only baseline. The proposed
method effectively handles implicit questions, delivers contextually relevant
answers, and tackles complex questions that rely heavily on conversational
context. The findings indicate that our method outperformed the evaluation
benchmarks up to 8% higher than existing methods and the LLM-only baseline.
