We are developing a corpus-based approach for the prediction of help-desk responses from features in customers? emails, where responses are represented at two levels of granularity: document and sentence. We present an automatic evaluation of the responses generated by our system, as well as a manual one involving human judges. The automatic evaluation involves textual comparisons between generated responses and responses composed by the help-desk operators. The results show that both levels of granularity produce good responses, addressing inquiries of different kinds. The human-based evaluation measures response informativeness, and confirms our conclusion that both levels of granularity produce useful responses.