The surveys of large language models (LLMs), covering their development, training, and applications. Key areas include data collection and preprocessing, which is crucial for model quality, and methods for adapting LLMs using instruction tuning or reinforcement learning with human feedback. The survey also discusses prompt engineering, which is important for task performance and involves designing clear instructions for the models. Additionally, the survey examines techniques like in-context learning and chain-of-thought prompting, and it addresses evaluation of LLMs in terms of factual accuracy and helpfulness. Finally, advanced topics such as long context modeling and retrieval-augmented generation are explored, along with techniques for improving efficiency.