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The paper discusses inference techniques for semiparametric models based on suitable versions of inference functions. The text contains two parts. In the first part, we review the optimality theory for non-parametric models based on the notions of path differentiability and statistical functional differentiability. Those notions are adapted to the context of semiparametric models by applying the inference theory of statistical functionals to the functional that associates the value of the interest parameter to the corresponding probability measure. The second part of the paper discusses the theory of inference functions for semiparametric models. We define a class of regular inference functions, and provide two equivalent characterisations of those inference functions: One adapted from the classic theory of inference functions for parametric models, and one motivated by differential geometric considerations concerning the statistical model. Those characterisations yield an optimality theory for estimation under semiparametric models. We present a necessary and sufficient condition for the coincidence of the bound for the concentration of estimators based on inference functions and the semiparametric Cramèr-Rao bound. Projecting the score function for the parameter of interest on specially designed spaces of functions, we obtain optimal inference functions. Considering estimation when a sufficient statistic is present, we provide an alternative justification for the conditioning principle in a context of semiparametric models. The article closes with a characterisation of when the semiparametric Cramèr-Rao bound is attained by estimators derived from regular inference functions.