What occurs on the back end when rebuilding the model?

When rebuilding the model, the system recalculates the relevance scores of documents, which directly impacts how documents are prioritized in document queues. This process involves analyzing the data again to ensure that the scores accurately reflect the current understanding of relevance based on whatever parameters or algorithms are in place.

Relevance scores are crucial because they determine which documents are most pertinent to user queries or cases. By recalculating these scores during the rebuild, the model can improve the retrieval and ranking of documents, ensuring that the most relevant information is readily accessible to users.

This process does not merely archive documents or update the user interface; these activities serve different functions and do not fully describe the model rebuilding process, which primarily focuses on recalibrating relevance based on the latest data and insights.