Several AI techniques are commonly employed in modern chatbot development to ensure effective communication and user engagement. One of the most fundamental techniques is Natural Language Processing (NLP), which enables chatbots to comprehend and interpret human language. NLP involves various tasks such as tokenization, which breaks down sentences into individual words or phrases, parsing and part-of-speech tagging to understand the structure of a sentence and the role of each word. Additionally, NLP allows for entity recognition, a subtask of information extraction that identifies and classifies named entities in text, such as a person's name, location, organization, contact details, time expressions, quantities, monetary values, and percentages.
Another important AI technique used in chatbots is machine learning, which enables chatbots to learn from conversations and improve their responses over time. Machine learning chatbots, also known as AI chatbots, can handle advanced, open-ended questions and provide natural responses. These chatbots employ speech recognition technologies to process text or voice commands and use AI-driven conversational AI technology to interpret and respond to customer and employee inquiries. Generative AI chatbots are also becoming increasingly popular across various industries, as they can generate human-like text based on the input data they receive, making them indispensable tools for providing personalized and engaging interactions with users.