Large language models (LLMs) and prompt-based learning as transformative tools have been widely used across both academic and industrial domains. This paper reviews the current landscape of LLM applications, examining their principles, methodologies, strengths, and limitations. Beginning with an overview of the evolution from traditional feature engineering to prompt-based approaches, it highlights the applications of LLMs in diverse fields, including bioinformatics, materials science, and drug discovery. The review further examines the technical intricacies of prompt-based learning, contrasting hard and soft prompt techniques and their respective contributions to optimizing model performance. Real-world implementations are analyzed, with a focus on applications in bioinformatics and financial technology, alongside a discussion of pressing challenges related to data security and privacy. This paper also investigates the economic value generated by LLMs and their potential societal and workforce impacts, such as industry disruption and new job creation. The anticipated trajectory of LLMs and their broader societal implications are discussed, emphasizing the need for continued research and policy to guide its responsible development.