What is domain adaptation in AI?

Domain adaptation in AI refers to the process of taking a model that has been trained on one domain and adapting it to perform well on a different, but related domain. This is particularly useful in situations where data in the target domain is scarce, and it leverages the knowledge gained from the original domain.

By adding organization-specific knowledge to AI prompts, you essentially enhance the model's understanding and performance in the specific use case relevant to that organization. This can involve fine-tuning the model with additional training on domain-specific data or modifying inputs to better reflect the terminology, context, and nuances of the new environment. Such adaptations help in improving the relevance and accuracy of the model’s outputs, making it more effective for the organization’s specific needs.

The other choices do not accurately describe domain adaptation and miss the concept of transferring and refining knowledge for improved performance in a new context.