AI, while not conscious, operates through complex algorithms known as models, which have become integral to various aspects of daily life due to advancements in training methods. The training process for AI models mirrors human learning, starting from basic skills to more complex tasks, using vast datasets to improve efficiency. Modern AI models are increasingly compact, requiring less data, and can run on everyday devices like smartphones and laptops. These models are now widely used across diverse fields, such as healthcare for speeding up diagnoses, pharmaceuticals for drug development, finance for trading pattern analysis, entertainment for content recommendations, and aerospace for planetary data analysis. The training involves several steps, including preparing clean and relevant data, selecting appropriate models, iterative training, validation, and deployment, while overcoming challenges like data quality and testing. As AI training evolves, with innovations such as few-shot learning, the potential applications continue to expand, bringing us closer to more sophisticated and seamlessly integrated AI solutions in everyday life.