The paper provides an introductory survey of Large Language Models (LLMs), covering their history, development, principles, applications, limitations, and future directions. LLMs have evolved from statistical language models (SLMs) to neural language models (NLMs) and pre-trained language models (PLMs) to achieve human-level text processing, understanding, and generation. The survey highlights the contributions of data diversity, computational advancements, and algorithmic innovations in driving LLMs' rapid progress. Key principles of LLMs, such as the transformer architecture and masked multi-head self-attention, are explained using the GPT-3 model as an example. Applications of LLMs in drug discovery, finance, medical, and legal fields are discussed, showcasing their potential to enhance efficiency and effectiveness. However, the paper also addresses limitations like bias, safety concerns, and intellectual property issues, emphasizing the need for future research to address these challenges. The survey aims to empower practitioners with a comprehensive understanding of LLMs to maximize their potential in various domains.The paper provides an introductory survey of Large Language Models (LLMs), covering their history, development, principles, applications, limitations, and future directions. LLMs have evolved from statistical language models (SLMs) to neural language models (NLMs) and pre-trained language models (PLMs) to achieve human-level text processing, understanding, and generation. The survey highlights the contributions of data diversity, computational advancements, and algorithmic innovations in driving LLMs' rapid progress. Key principles of LLMs, such as the transformer architecture and masked multi-head self-attention, are explained using the GPT-3 model as an example. Applications of LLMs in drug discovery, finance, medical, and legal fields are discussed, showcasing their potential to enhance efficiency and effectiveness. However, the paper also addresses limitations like bias, safety concerns, and intellectual property issues, emphasizing the need for future research to address these challenges. The survey aims to empower practitioners with a comprehensive understanding of LLMs to maximize their potential in various domains.