Ocelot teaches our AI model about the numerous program options at a school by creating comprehensive questions such as "What majors or programs are available?" and training it with hundreds of program-specific examples such as “Do you offer nursing?”  In essence, we are combining programs into one broad question as we have found it greatly increases the chances of users getting the information they need and is significantly less upkeep for our clients. If we did not make this decision, there would be thousands of program offerings and questions for clients to review individually, and it would stretch our AI model in a manner that would make it less effective for users. 

Similarly, Ocelot has trained our AI model to recognize numerous location options by creating broad questions such as "Where is the school located?" and training it with specific examples such as "How many campuses does the school have?" 

Additionally, chatbot users may not know the exact title of an institution's specific program or precise campus location, and customizing the response to these comprehensive questions provides a much better user experience. For example: an "English" major may be called "English Language and Literature," “English Literature," or "English Language and Writing" etc. A prospective student is the most likely to just ask about an institution's "English major." Likewise, users from outside the Chicagoland area may not make a distinction between the city of Chicago and the city of Naperville and may use "Chicago" to reference both.

With this structure in mind, we cannot add individual questions about programs, classes, or locations since we do not have the framework and training data to effectively parse out each and every program, course option, or location name as individual questions. Our current structure provides the highest chance for a user to get the information they need through a direct knowledge base response, suggestion box, or search link.