Large language models (LLMs) are a type of AI model that has revolutionized how AI works, and they have numerous applications in academia, tech, entertainment, and the research community. They are trained on extensive data sets and utilize intricate architectures like stack or encoder-decoder structures to perform computations through operations such as convolution and attention mechanisms. LLMs can be enhanced by using advanced statistical techniques like reinforcement and transfer learning, which allows them to handle diverse inputs, including comprehension, synthesis, translation, question answering, and image & text generation. The training process of LLMs involves two primary steps - initialization and iterative improvement through backpropagation gradient descent, with pretraining techniques such as masked language modeling exposing the model to various languages. Fine-tuning follows initialization and can be combined with unsupervised and supervised learning to enhance a model's capabilities. Despite challenges like limited availability of computational resources, LLMs have become a common part of everyday life and are transforming various aspects of human interaction and business operations, including enhancing digital communication, simplifying content creation, improving language translation, coding assistance, finance industry, shopping experiences, education, media and entertainment, and more. Measuring the effectiveness of LLMs involves using quantifiable indices relative to benchmarks like human-generated responses or expert-derived ground truth solutions, and evaluating metrics include BLEU scores, ROUGE metrics, F1-scores, exact match percentages, and METEOR values. As LLMs continue to evolve, their impact on everyday life and various industries is expected to grow, revolutionizing human-machine interaction and business operations.