Upload any PDF. Set a page range. Get answers drawn exclusively from those pages, with exact page numbers cited. No hallucinations. No off-syllabus content.
Study Mode is LumiChats' most-used feature among students. Standard AI chat has a critical flaw for academic work — it answers from its entire training data, which often conflicts with your textbook's definitions, notation, or framing. Study Mode fixes this by pinning the AI to your uploaded document. Every answer is grounded in your material, not the internet.
Drag and drop any PDF — textbook chapter, lecture slides, research paper, or past exam paper. LumiChats processes it and indexes every page for retrieval.
Tell the AI which pages to focus on. 'Pages 30 to 60' means every answer is drawn only from those pages. Perfect for chapter-by-chapter exam prep.
Type questions in plain language. The AI retrieves the relevant section from your document and answers using only that content, citing the page number in its response.
Once you've studied a section, switch to Quiz Hub. Generate a quiz on the same page range using the same document. Active recall, perfectly aligned to your material.
At the end of your session, ask LumiChats to generate structured notes from everything you covered. Get a revision-ready summary in seconds.
Upload your entire textbook PDF. Study chapter by chapter without the AI confusing content from different sections. Perfect for semester exams.
Upload a 60-page journal paper. Ask LumiChats to explain specific sections, summarise arguments, or identify the research methodology — all cited to the exact page.
Export your professor's slides as a PDF. Ask questions about specific slides without worrying about the AI inventing content that isn't in the presentation.
Upload past exam papers alongside your textbook. Ask LumiChats to find the textbook section that answers each exam question.
Technical Note
Study Mode uses LumiChats' RAG (Retrieval Augmented Generation) pipeline. Uploaded documents are split into 4,000-character chunks with overlap, embedded using a text embedding model, and stored in a vector database. When you ask a question, your query is embedded and the most semantically similar document chunks are retrieved and passed to the AI as context — ensuring answers are grounded in your document, not the model's training data.
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