We present Automatic Differentiation (AD), a technique to obtain derivatives of functions provided as programs. We present the principles that justify why AD is possible and explain its performance. The adjoint mode of AD is the choice approach to obtain gradients, like the gradients needed for data assimilation. We show the specific difficulties of the adjoint mode, and list a few AD tools that handle these problems well. We show why AD needs an enlightened user to achieve optimal efficiency.