The chain rule is a core idea from calculus that explains how a change in one variable influences another when multiple functions are linked together. In simple language, if one quantity affects a second, and that second affects a third, we can find the overall impact by multiplying their derivatives. This idea becomes essential in artificial neural networks, where each layer transforms its input before passing it forward. During training, the network uses backpropagation to update its weights.