In this descriptive and exploratory study, 32 users type help requests to what they believe is a computerized advisor. In fact, the advisor is a human mimicking realistic levels of intelligence and knowledge that can be expected from a computerized advisor. Results show that users request help with a very simple and restricted language that is characteristic of language generated under real-time production constraints and of child language. Moreover, users' utterances are frequently ungrammatical. It is hypothesized that these features arise from factors intrinsic to typed advisory situations: Users are performing a primary task under real-time constraints, and typing help requests is a secondary task. On the other hand, users refer to objects and events with very precise descriptions instead of faster-to-type pronouns; they produce very few ellipses and deictic expressions. Future research should elucidate whether shared context between users and computerized advisors needs to be richer than created in this study to sustain the use of expressions whose interpretations depend on context. The tuning of natural language interfaces to the features observed in this study may increase the usefulness of natural language interfaces to advisory systems. The presented methodology is a promising tool for further studies of these factors on users' language.