Gradient descent is a step-by-step method used in machine learning to improve a model by reducing its errors. It does this by adjusting the model's settings based on how much it is "off" from the desired outcome. The algorithm calculates the direction and amount of adjustment needed and gradually moves the model towards the best possible performance.
"We used gradient descent and user feedback to tune the model to find the answer most likely to please people using the chatbot."
