Common questions from educators and students.

A raw upload gives the AI a messy, flattened version of your material. Ellide prepares the course document for AI use first, which makes the content easier to work from and lets teachers shape the tutoring behavior instead of accepting a generic chatbot response.

The intended product experience is that students see a clean study document while the AI receives the guidance layer inside the prepared output. The visible material should stay readable.

You can push the interaction toward guided tutoring behavior, such as asking a question first, holding back the final answer, or pointing students to the relevant page or diagram. That does not make every model perfect, but it gives you more control than a raw upload does.

Syllabi, lecture slides, handouts, assigned readings, scanned PDFs, study guides, and course packets are the core fit. The best results come from material that already reflects how the class is taught.

The product direction is to keep the student path simple. Students should be able to work with prepared study material in the AI tools they already use rather than needing a complex new workflow.

The product story is built around minimizing unnecessary student exposure, keeping the visible materials clean, and avoiding model training on uploaded content. Any production privacy claims should stay aligned with the actual backend implementation.