A Case Study on the Generative AI Project Life Cycle Using Large Language Models

Abstract

Large Language Models represent a disruptive technology set to revolutionize the future of artificial intelligence. While numerous literature reviews and survey articles discuss their benefits and address security and compliance concerns, there remains a shortage of research exploring the implementation life cycle of generative AI systems. This paper addresses this gap by presenting the various phases of the generative AI life cycle and detailing the development of a chatbot designed to address inquiries from prospective students. Utilizing Google Flan LLM and a question-answering pipeline, we processed user prompts. In addition, we compiled an input file containing domain knowledge of the education program, which was preprocessed and condensed into vector embeddings using the HuggingFace library. Furthermore, we designed a chat interface for user interaction using Streamlit. The responses generated by the chatbot are both descriptive and contextually pertinent to the prompts, with their quality improving in response to more detailed prompts. However, a significant constraint is the size limit of the input file, given the processing power limitations of CPUs.