Automatic Differentiation (AD) is a computational technique that efficiently and accurately evaluates derivatives of functions expressed as computer programs. Unlike symbolic differentiation, which can be slow and error-prone, and numerical differentiation, which can suffer from precision issues, AD uses the chain rule to decompose derivatives into a series of elementary operations, ensuring both speed and precision.