Answer
It works by first making a prediction, measuring the error, and then sending that error backward through the network layer by layer to update each weight using gradient descent.
Real-world example When a neural network mislabels a cat photo as a dog, backpropagation adjusts the internal weights so the next prediction is more likely to be correct.