  In the rapidly evolving domain of artificial intelligence, chatbots have
emerged as a potent tool for various applications ranging from e-commerce to
healthcare. This research delves into the intricacies of chatbot technology,
from its foundational concepts to advanced generative models like ChatGPT. We
present a comprehensive taxonomy of existing chatbot approaches, distinguishing
between rule-based, retrieval-based, generative, and hybrid models. A specific
emphasis is placed on ChatGPT, elucidating its merits for frequently asked
questions (FAQs)-based chatbots, coupled with an exploration of associated
Natural Language Processing (NLP) techniques such as named entity recognition,
intent classification, and sentiment analysis. The paper further delves into
the customization and fine-tuning of ChatGPT, its integration with knowledge
bases, and the consequent challenges and ethical considerations that arise.
Through real-world applications in domains such as online shopping, healthcare,
and education, we underscore the transformative potential of chatbots. However,
we also spotlight open challenges and suggest future research directions,
emphasizing the need for optimizing conversational flow, advancing dialogue
mechanics, improving domain adaptability, and enhancing ethical considerations.
The research culminates in a call for further exploration in ensuring
transparent, ethical, and user-centric chatbot systems.
