Fine-tuning adapts a pre-trained machine learning model to perform well on new tasks. Data scientists call these "foundation models." Foundation model providers train these enormous neural networks on millions of documents. This diversity of training data allows foundation models to perform many tasks that they were not specifically trained for—though at low accuracy.

Instead of starting from scratch, fine-tuning allows data scientists to build upon these foundations. They feed the models additional input and output examples to adapt them to specific business use cases.

This approach reduces the time and resources needed to deploy AI solutions while maintaining high performance.