Large Language Models (LLMs) are pivotal in the ongoing AI boom, significantly impacting industries reliant on language processing, such as healthcare, finance, and education. These models, like ChatGPT, have made AI more accessible to the public, showcasing near-human performance levels. LLMs function by predicting the next element in a text sequence through deep learning techniques, utilizing transformers and self-attention mechanisms. They are trained on vast datasets, often sourced from the internet, which presents challenges related to bias, privacy, and ethical considerations. Despite their impressive capabilities in generating text and applications in chatbots, code generation, and content creation, LLMs face limitations in logical reasoning and maintaining security. As foundational models, LLMs are typically fine-tuned for specific tasks, and their training demands substantial computational resources, often accessible only to elite companies. The article underscores the importance of understanding the technical and security aspects of deploying LLMs, while also addressing emerging research areas and potential risks associated with their use.