A hyperparameter is a configuration setting for a machine learning model (e.g., learning rate, number of layers) that is set before training begins. These parameters control the learning process itself and significantly impact model performance.

NYD Application: We tune hyperparameters in our AI tools to optimize performance for specific use cases like code analysis or design pattern recognition.

Example: "We adjusted the learning rate hyperparameter to make our model converge faster on Lovable component predictions."