The term "fine-tuning" of foundation models originates from the fields of Artificial Intelligence, Digital Transformation, and Big Data and Smart Data. Foundation models are large, pre-trained artificial intelligence models that have learned from vast amounts of data, such as language or image models. Fine-tuning means that these general models are subsequently adapted for specific tasks or company data.
Imagine a Foundation Model as an employee who already has many skills but is new to your industry. Through fine-tuning, this employee then learns specific skills – such as special technical terms or typical processes within your company. This saves time and money because you don't have to train a model from scratch every time.
Example: An insurance company uses a Foundation Model for processing claims. To ensure the model understands industry-specific terms and typical phrases from the insurance sector, it is further trained using its own insurance data through fine-tuning. This allows the artificial intelligence to deliver more reliable results, adapt better to the company's requirements, and thereby improve operational efficiency.
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