Standard procedures for estimating the item parameters in IRT models make no use of auxiliary information about test items, such as their format or content, or the skills they require for solution. This paper describes a framework for exploiting this information, thereby enhancing the precision and stability of item parameter estimates and providing diagnostic information about items' operating characteristics. The principles are illustrated in a context for which a relatively simple approximation is available: empirical Bayes estimation of Rasch item difficulty parameters. (49pp.)