Authors - Austin jojo Jallah, Reena Satpute Abstract - As the transformer language models were developed, it was possible to introduce one of the significant technological revolutions in conversational AI. Basically, the understanding and production of human language has been enhanced by transformers. Transformer models make it possible to build dialogue systems that provide natural, complex, and context-sensitive communication to develop further customer support technologies, healthcare technologies, and virtual assistance services. In the current paper, this paper will assess advanced transformer systems, BERT, GPT, and T5, as well as their variants, assessing their performance capabilities in the use of dialogue. All the models are evaluated in terms of performance quality, which is judged by its fluency production and also gauges of coherence and contextual accuracy and its general ability to pro-cess computations. This review talks about the prompt engineering approaches and the human feedback-enhanced learning through reinforcement (RLHF) and the adapter transfer learning methods, to improve the flexibility and quality of the model. The paper presents the fresh trends in Conversational AI with multi-modal learning as well as retrieval-enhanced generation and factuality-based coherence knowledge application. Nevertheless, transformer-based models have three key weaknesses, among which, there are bias and hallucinations, and the computing requirements are very high. These weaknesses are analyzed and we discuss the potential remedies that involve symbolic deep learning combinations and efficient compression methods, such as quantification and pruning. We have found out the extent to which each system can do and the things that they cannot accomplish, and hence can help to determine the right applications of each of the frameworks. We introduce an evaluation comparison as a scholarly resource to the practitioners of dialogue system development so they can perform better with moral and effective models of operations. The article concentrates on the devel-opment of transformer-based conversational AI and its estimated impact on hu-man-computer dialogue systems
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