Elicit vs NotebookLM

Option A

Elicit

Paper search and extraction tables for people running something like a systematic review.

Best for

• Evidence tables, not vibes
• Researchers who will write inclusion criteria
• People extracting outcomes across a paper set

Strengths

• Forces the work into a table you can audit
• Closer to review practice than a chatbot

Trade-offs

• Garbage criteria produce garbage grids
• You still have to read the papers you will cite

Verdict

Elicit belongs on a research desk when the deliverable is a screened set of papers plus extracted fields. It does not belong there when you wanted a cited news brief or a chat with one PDF. Consensus, SciSpace, Scite, and the graph tools each own a different slice of the same afternoon. Keep Elicit for the table. Confirm extraction quality on two papers you already know before you trust a 40-row export. If you will not write inclusion criteria, you will not get a systematic review — you will get a sorted hallucination.

Option B

NotebookLM

Answers fenced by the files you uploaded — syllabi, policies, meeting exports — not the open web.

Best for

• Bounded corpora you actually selected
• Study packs and onboarding sets
• Audio overviews of material you already trust

Strengths

• The fence is the feature
• Good for learning a packet you already have

Trade-offs

• Not a search engine
• Uploads are a policy decision

Verdict

NotebookLM is for a packet you chose. Perplexity is for the web. Elicit is for papers you have not gathered yet. ChatGPT is the general chat that will happily ignore your fence. Upload only what the room is allowed to see. Ask a question you can falsify against the PDF. If the answer cites nothing you uploaded, throw it away. Confirm sharing on the notebook before you invite a class or a client. Ask a question whose answer you already highlighted in the PDF. If NotebookLM misses it, do not trust it on the parts you have not read. The fence only helps if you check the gate.