How to Chat With Research Papers Using AI: A Literature Review Workflow

To chat with research papers using AI, you upload a stack of PDFs — the papers for a literature review, a thesis chapter's source list, a reading pile for a seminar — into one project, then ask questions across all of them instead of opening each PDF and reading it start to finish. “Which of these papers used a randomized design?” or “What sample size did the third study use?” gets answered by pulling the relevant passage from whichever paper actually contains it, with a citation pointing back to the source.

Under the hood it's the same retrieval-augmented generation covered in our RAG explainer: each paper is split into chunks, embedded, and retrieved by relevance to your question. The only thing that changes with a stack of academic papers instead of one document is scale — a literature-review question has to search across every paper in the project, not just one.

1. Create one project for the review or course, not a separate one per paper — grouping related papers together keeps every question scoped to the right stack.
2. Upload every PDFyou want to draw on — there's no need to skim each one first to decide if it's relevant.
3. Wait for each paper to finish indexing before asking a question that spans more than one of them.
4. Start broad: “What methods do these papers use?” or “Summarize the main findings of each.”
5. Narrow down with specific follow-ups once you know which papers are actually relevant to the question you're trying to answer.

The real advantage over reading each PDF separately shows up once you ask a question that spans the whole stack: “Which of these papers report a significant effect, and which don't?” or “How do these three studies define the outcome variable?” A tool built for document Q&A pulls the matching passage from each paper that addresses the question and cites every one of them, instead of you re-reading each PDF to build that comparison table yourself.

This is the same mechanism behind comparing documents with AI, just applied to papers instead of contract drafts: a direct comparison question does the matching work for you, and you check the citation on each side rather than scanning both PDFs by eye.

A quoted answer that can't be traced back to a page and passage is useless in a literature review — you need to know exactly which paper said what before you can cite it yourself. That's the same principle behind document Q&A with citations: every answer should point back to the exact passage it came from, so you can quote it correctly instead of paraphrasing from memory. It matters even more for the small exact terms a literature review runs on — an author's name, a p-value, a sample size — the kind of detail that gets missed if a tool only searches by meaning instead of also matching the literal text.

Doxy is built for exactly this workflow: create a project, add every paper as a PDF, and ask questions across the whole stack with answers cited back to the exact paper and passage. It works as an AI research assistant for a literature review and as a NotebookLM alternative for everything else in the same project — your own notes, a recorded seminar, or a dataset the papers reference. Upload your first batch of papers and ask the comparison question you've been putting off.