Abstract

The rapid evolution of conversational artificial intelligence has led to growing interest in intelligent chatbots capable of understanding and generating natural language with human‑like fluency. Central to these advancements are transformer‑based natural language processing (NLP) models, which leverage self‑attention mechanisms to capture long‑range dependencies and contextual semantics in text. This paper explores the development, implementation, and evaluation of intelligent conversational chatbots built using transformer architectures such as the Transformer, BERT, GPT, and related variants. We examine the architectural innovations that distinguish transformer models from earlier recurrent and convolutional approaches, emphasizing how attention mechanisms improve dialogue coherence, context depth, and response relevance. A comprehensive literature review surveys key research milestones and practical chatbot systems, identifying strengths and limitations of transformer‑enhanced conversational agents. We present a structured research methodology detailing dataset preparation, model training, fine‑tuning, evaluation metrics, and deployment considerations. Performance advantages such as adaptability to diverse domains, contextual awareness, and scalability are discussed alongside challenges including computational costs, training data biases, and response safety. Results from comparative evaluations with traditional chatbot frameworks highlight significant gains in linguistic quality and task performance. The paper concludes with insights regarding best practices and future research directions to advance conversational AI toward more robust, versatile, and ethically sound systems.