Choosing an AI paper search tool:search in one place, read in another

Traditional search engines like PubMed and Google Scholar return a list of papers matching your keywords. The novelty of AI paper search tools is that they respond to a question with an answer, grounded in real papers.

As an entry point, that's genuinely transformative. But precisely because the response arrives as an answer, the line between "getting an answer" and "checking a paper" blurs easily — and that's the new failure mode to watch for.

Most of the popular tools fall into one of four types.

Question-answering tools (Consensus, Perplexity, and similar) summarize what the literature says in response to a question — useful for getting oriented in a field. Research-support tools (such as Elicit) gather papers relevant to a research question and organize their key points — useful for building a reading list. Conversational tools (ChatGPT and similar) are better as advisors for crafting search terms and sharpening your question than as search engines themselves. And traditional databases (PubMed, Google Scholar) remain the main arena whenever you need comprehensive, reproducible results. Traditional databases are still the foundation.

Before you rely on any paper surfaced by an AI search, run through these checks.

Does the paper actually exist? (Search its title in the original database.) Are the DOI, journal, and publication year correct? And above all, check the substance — participants, methods, main results, limitations — in the original PDF, not in the AI's description. The AI's summary is only a doorway; the evidence always lives in the original text.

The more AI search tools you use, the more a familiar problem creeps in: the papers you find end up scattered across each tool's history, bookmarks, and open tabs.

My recommendation is to separate the two roles from day one: search wherever you like, but read in one place. Collect the PDFs of every paper you find — from any tool — in a single home. That alone eliminates the "where did I see that paper again?" problem.

Paperfy is a library built for exactly that "place to read." Upload the PDF of a paper you found through AI search, and the AI-generated summary, the figures and tables, and the original text come together on one page per paper.

I skim the summary first, then check the original text, and edit the summary by hand to capture anything the AI misread — along with my own insights — as research notes. Whatever tool I used to find a paper, it ends up as knowledge I can return to later.

To sum up: field overview → question-answering tools. Building a literature list → research-support tools plus traditional databases. Help with search terms → conversational tools. Rigorous, reproducible searching → traditional databases.

And for every tool: decide in advance where the papers you find will live, and make checking them a routine. That design choice shapes your results more than which tool you pick.