Machine learning models are designed to take in data, to find patterns or relationships within those data, and to use what they have learned to make predictions or to create new content. The quality of those outputs depends not only on the details of a model's inner workings but also, crucially, on the information that is fed into the model. Some models follow a brute force approach, essentially adding every bit of data related to a particular problem into the model and seeing what comes out.