Using an AI Research Assistant for a Literature Review Without Losing Your Sources

October 5, 2026 · 5 min read

The assistant writes; you keep the record. Sources get lost for a simple reason: the work happens inside a conversation window that scrolls, closes, or gets summarized again, and nothing in it is designed to survive as evidence. The fix is not a better prompt. It is a working file that you own, where every source gets a row the moment it enters the review and every claim the assistant produces gets marked as verified or not. The AI fills the rows faster. The file is what stands on its own.

What should be in place before the first search

Three things, all cheap, all yours.

First, the scope in one sentence: the review question, the boundaries, the time window. Write it at the top of the file. It is the anchor that tells you when the assistant, or you, has drifted.

Second, the tool's lane. SentX's paper summarizer takes a pasted excerpt or a shareable PDF and returns a structured reading: the paper's question, method, results, and limitations. It is an extraction aid for material you can paste or upload, not a database search engine, and none of the sources checked for this article measures how often its readings are complete or correct. Plan on every reading going back to the original. SentX publishes this article and develops the tool, so the limits in this piece come from reading its own pages critically rather than from an independent benchmark.

Third, the upload rule. SentX's published privacy policy states that submissions are not confidential, that retained interactions can enter shared memory and influence other users' interactions, and that deleting an account does not promise removal of material already incorporated. That is a description of the published policy, not a legal opinion, but it settles the practical boundary: use excerpts or PDFs you are permitted to share, and keep confidential material out.

What should the working file look like

One spreadsheet or document, one row per source. Columns that earn their place: the identifier (title, authors, year, DOI); where and when it was found (query, database, date); what it supports, in one line of your own words; and a status — verified against the original, summary only, or assistant-suggested and not yet located.

An illustrative row, invented for this article: "Chen et al., 2022, DOI 10.0000/illustration, found via query on 3 Sep, supports: treatment group improved retention over control, status: summary only, p. 4, Table 2." Everything in that row is fictional — the paper, the authors, and the finding — so treat it as a template, not a result. The shape is the point. Each column maps to a question a future reader — a supervisor, a co-author, or you three months later — will actually ask.

How do you run the review

1. Search yourself, log everything. The assistant can suggest queries and angle variations, but you run them and record each search — query, source, date, result count — as its own entry. This is the step people skip most, and the one that separates a traceable review from a chat transcript.
2. Extract in batches, verify individually. Feed papers through the summarizer and copy the structured fields straight into the row. Then open the original and confirm the load-bearing details: the numbers, the population, the units, the exact claim you will quote. The tool's own page warns that output may omit or misread details; that warning is why the status column exists. Where the assistant read a figure or a chart, slow down further — none of the sources checked for this article establishes that it interprets visual content reliably.
3. Mark what the assistant said versus what the paper says. When the assistant offers a comparison — study A found X while study B found Y — that is its synthesis, not either paper's finding. File it as assistant inference and check both sides before it enters the review.
4. Resolve every citation. Any reference the assistant names gets a DOI or publisher-page check before it is trusted. A confident, well-formatted citation is not evidence that the paper exists or says what is attributed to it.
5. Checkpoint at breakpoints, not at the end. At the close of each session, update the file with what is decided, what is verified, and what is open. Resuming days later — in the same tool or another — start by pasting the checkpoint and having the assistant restate it, then compare item by item. The conversation forgets; the file does not. You are the continuity layer, and the file is how you carry it across sessions.
6. Draft with the assistant, synthesize yourself. Clustering themes, listing gaps, comparing directions of effect are good first-pass tasks. The judgment calls — which findings actually bear on the question, where studies genuinely conflict, what the evidence cannot show — are yours, and the file records them with reasons.

What does the finished review prove, and what does it not

Done well, the review is backed by a file that stands alone: what was searched, when, what was read, what was verified, what remains open. That is the operational meaning of not losing track.

Two limits worth stating plainly. First, this workflow makes an informal review traceable; it does not make it a systematic review. PRISMA 2020 defines a 27-item reporting checklist and flow diagrams for systematic reviews, and an AI-assisted summary workflow, however neatly logged, is not evidence of compliance with that guideline or of a comprehensive search. If the deliverable is a systematic review, write the protocol first and report against the checklist — the assistant works inside that discipline, not instead of it. Second, among the sources checked for this article, no benchmark measures how often the summarizer's readings are complete or accurate. The verification steps above are therefore not optional overhead; they are the method.