Foundation models are expansive AI-trained systems that utilize vast datasets and computational power to produce diverse outputs, including text and images, and are integral to popular tools like ChatGPT, DALLE-2, and BERT. Developed with principles such as pre-training on large datasets and self-supervised learning, these models are adaptable for various tasks, including image classification and natural language processing. The term "Foundation Models" was introduced by Stanford's HAI Center, and these models are seen as transformative for AI system development. Notable examples include large language models (LLMs), generative adversarial networks (GANs), and multimodal models, each with applications in fields ranging from healthcare to satellite imagery analysis. Evaluation metrics for these models include precision, F1 score, and others, tailored to measure performance in specific contexts. The impact of foundation models is evident in their ability to lower entry barriers for AI adoption across industries, fostering the integration of AI tools in organizational operations and projects.