Large Language Models (LLMs) are becoming increasingly integrated into companies' product offerings due to their superior capabilities compared to previous neural network-based models. They can perform reasonably well with only a few labeled samples of data and scale adequately with an increase in parameters and training data. Core application areas for LLMs include summarization, translation, conversational AI, and text-to-code generation. As these models grow larger and more sophisticated, their capacity to serve as human-computer interfaces for various tasks becomes a potential reality.