This chapter explores three major NLP applications: (1) Information Retrieval Systems (IR), (2) Text Summarization Systems (TS), and (3) Question-Answering Chatbot Systems (QA Chatbots). IR refers to the process of extracting relevant information from large-scale unstructured data, in contrast to traditional structured database records. This data can come from various sources, including text, images, audio, and video. IR systems are not limited to search engines; they also include recommendation systems, such as those used by e-commerce platforms, as well as question-answering and interactive systems. Text summarization involves reducing a large dataset by computationally generating a concise summary that captures the most relevant information. This is crucial for NLP tasks such as text classification, question-answering, legal text processing, news summarization, and headline generation. The QA system represents a human–machine interaction framework where human natural language serves as the communication medium. It is a task-oriented system designed to answer specific questions or objectives through dialogues, often incorporating sentiment analysis. Additionally, this chapter provides the conceptual foundation for Workshop #7—Building a Chatbot with TensorFlow and Transformer Technology, discussed in Chap. 17 .