The advancement of AI technology has led to the widespread use of AI customer service chatbots across sectors such as e-commerce, education, and banking. In e-commerce, businesses integrate these chatbots with human customer service to cut costs and boost efficiency. Despite these advantages, the robots sometimes fail to accurately understand queries or provide irrelevant responses, raising questions about these shortcomings. This overview explores the underlying principles of AI customer service chatbots and the causes of their occasional failures in understanding inquiries. Working Principles of AI Customer Service Chatbots The operation of AI chatbots can be outlined as follows: Data Input, Data Processing, Data Output, and Feedback Learning. 1. Data Input: – Users interact with the robots via text, images, or voice to express their questions or needs. 2. Data Processing: – Natural Language Processing (NLP): Robots use NLP techniques, including tokenization and syntactic parsing, to interpret user input and discern intent. – Knowledge Base and Dialogue Management: Robots utilize a pre-existing knowledge base and a dialogue management system to generate responses based on user intent. 3. Data Output: – Robots create replies and communicate them back to users in text, speech, or image form, guided by the user’s intent. 4.
