Rating systems not only provide information to users but also motivate the rated agent. This paper solves for the optimal (eﬀort-maximizing) rating system within the standard career concerns framework. It is a mixture two-state rating system. That is, it is the sum of two Markov processes, with one that re-eﬀects the belief of the rater and the other the preferences of the rated agent. The rating, however, is not a Markov process. Our analysis shows how the rating combines information of diﬀerent types and vintages. In particular, an increase in eﬀort may aﬀect some (but not all) future ratings adversely.