Foundation Model

A foundation model is trained on broad datasets at large scale so it can serve as a base for many downstream applications. Rather than learning only one narrow task, it develops general capabilities that can be accessed through prompting, connected tools, retrieval, or additional training. Large language models are a familiar example, while other foundation models work with images, audio, video, code, or several data types together. Building them requires extensive computing resources and careful data preparation, but many organizations use existing models through hosted services or open weights. Their broad reuse also spreads underlying limitations: bias, security weaknesses, licensing concerns, and factual errors may affect numerous products unless developers evaluate the model within each specific context.