LLM-based chatbot systems for real world applications

This thesis explores the development and application of a chatbot that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to address real-world challenges and use cases. The focus of this research is to demonstrate how these cutting-edge technologies can be integrated to create an efficient and context-aware conversational agent. By incorporating RAG, the chatbot is capable of retrieving relevant information from vast datasets, making it particularly valuable in scenarios where real-time, accurate responses are crucial, such as customer support, technical assistance, and personalized recommendations. The initial chapters provide a comprehensive overview of foundational concepts, including Natural Language Processing (NLP), Generative AI (GenAI), and the evolution of LLMs. These sections not only introduce the fundamental principles of language understanding and generation but also trace the historical progression of these technologies, highlighting key breakthroughs and their impact on the field. In Chapter 5, the technical implementation of the ChatWolf software is thoroughly examined. This includes the decision-making process behind selecting RAG and LLMs as core components, as well as the integration of a vector database (VecDB) to enable efficient information retrieval. The second section of this chapter focuses on the available interfaces of ChatWolf, providing a detailed description of each interface and discussing the rationale behind the design choices. Finally, the concluding chapter of this thesis reflects on the impact of these advanced technologies on today's society and the changes they are likely to bring about in the near and distant future. The chapter also mentions the ethical challenges associated with these advancements. As generative models become more pervasive, balancing innovation with ethical considerations becomes essential, ensuring these powerful tools benefit society as a whole.