What happens during the "training phase" of Generative AI models?

During the "training phase" of Generative AI models, the primary focus is on the model learning from the training dataset. This process involves the model analyzing and processing a large amount of data to identify patterns, relationships, and structures within the data. The goal is to enable the model to understand the characteristics of the data so it can generate new, relevant outputs based on what it has learned.

This learning phase is crucial because it establishes the foundational knowledge that the model will use to create content, whether that be text, images, or any other form of generative output. By adjusting its internal parameters based on the training data, the model optimizes its performance for the task it was designed for.

In contrast, predicting future outcomes occurs later in the model's usage, where it applies what it has learned in practical scenarios. Evaluating user feedback happens after the model has been deployed, influencing how it might be fine-tuned or retrained in the future. Similarly, generating independent outputs refers to the model's capabilities after training, where it can utilize its learned knowledge to create new content rather than learning from data.