Large language models have been a part of computer science for over 57 years, with the first being ELIZA in 1966, which used natural language processing to mimic human conversation. These models have evolved significantly since then and are now powered by transformer neural networks, which allow them to process sequential data and generate human-like responses. With their ability to learn from vast amounts of data and simulate human thought processes, large language models can be highly usable for specialized tasks and even generate new text in seemingly human language. However, they also have limitations, such as the risk of hallucination and going rogue, and require human fact-checking and sign-off to ensure accuracy and reliability. Despite these challenges, large language models are proliferating and being used in various industries, including retail, technology, and healthcare, where they can perform tasks such as sentiment analysis, machine translation, and natural language processing.