## 1 Introduction  
Currently, ChatGPT can integrate applications for real-time tracking of many areas of a smart city such as traffic management, energy management [1], environmental monitoring, healthcare [2], and emergency response. ChatGPT can integrate applications of smart cities in real time with effectiveness and efficiency. Generative artificial intelligence (hereafter termed GenAI) is a rapidly developing technology that has gained significant traction, which has arguably been driven by the release of ChatGPT by OpenAI (OpenAI: [[https://openai.com/](https://openai.com/)]([https://openai.com/](https://openai.com/)) (accessed on 10 December 2023)). In practice, GenAI is an important example of a disruptive innovation (DI) where novel technologies can result in technological determinism (TD) [3]. GenAI has been the subject of many ethical, societal, technological, and practical risks expressed by a diverse range of stakeholders as discussed in Section 2. GenAI models have gained traction in multiple domains of interest, and the influence exerted by GenAI is clear as shown by research studies published in the literature. The design and development of GenAI models is highly resource intensive, requiring a large investment in financial, technological, computational, social analysis, and human resources [4; 5; 6]. Additionally, data corpus-assisted data-driven learning remains a critical element [7] and there is a need for a suitable large language model (LLM) [8]. While there are cloud-based options (a range of models and plans) available from GenAI developers (for example see the OpenAI ChatGPT plans and OpenAI pricing: [[https://openai.com/pricing](https://openai.com/pricing)]([https://openai.com/pricing](https://openai.com/pricing)) (accessed on 10 December 2023))), GenAI models are generally domain-specific and the resource intensive nature of such models along with the related LLMs limits the ability of small-medium enterprises (SMEs) to adopt an appropriate GenAI model. Identifying a resolution to this problem is important as chatbots can offer significant organisational and commercial benefits for organisations of all types [9; 10; 11]. Recently, many applications for large language models (LLMs) have considered reasoning mechanisms in LLMs for reasoning and making decisions using ChatGPT. The state-of-art characteristics of GPT models can be combine with the language understanding capabilities applied to many application domains [12; 13]. An approach of using reasoning techniques has been performed by using distinct datasets of GPT-3.5, GPT-4, and BARD models [14]. All the models mentioned above deal with high costs and GPU hardware requirements, so medium-size companies or organisations in cities lack the high-cost hardware resources needed to run ChatGPT. Compared to these studies, LLMs require a large infrastructure or system for reasoning and answering questions [12]. Our study can develop LLMs and ChatGPT deployed on medium-size GPU servers which are suitable for SME infrastructure. The latest versions of ChatGPT models and Google's BARD [13; 14] have been used in the evaluation of reasoning domains such as deductive, inductive, and question-answering tasks. Both ChatGPT and BARD may sometimes produce plausible but incorrect outcomes and inaccurate interactions in large domains. We have investigated BLOOM [15] for improving the accuracy in question-answering challenges and the model's performance in dealing with a variety of application domains that suitably consider a medium-size infrastructure. In this paper, we present our proposed method (termed _Expert-B_) which utilises open-source program code based on BLOOM [15] as its backbone.