Does Gemini Notebook (formerly NotebookLM) Hallucinate? How to Check

You ask your notebook a question and get a tidy answer with small numbered citations. It looks checked. But does Gemini Notebook (formerly NotebookLM) hallucinate anyway? Yes, sometimes, and less often than a general chatbot. Its mistakes are also quieter: a summary that leans harder than the source does, or a number taken from the wrong row. This guide explains what “grounded” actually promises, what published studies measured, where Gemini Notebook slips, and a six-step routine to check an answer before you repeat it.

Key Takeaways

• Yes, it can hallucinate. In a 2025 newsroom-style test, 2 of 15 NotebookLM answers (13%) had a hallucination, against 40% for ChatGPT and Gemini (Hagar et al., 2025).
• It works like a RAG system: it retrieves passages from your sources, writes from them and cites them.
• “Grounded” means faithful to your sources, not true. A wrong source gives a faithfully wrong answer.
• Most slips are overconfident readings, misplaced emphasis and misread numbers, not invented facts.
• Open the citation, ask for exact quotes and ask what the sources don’t say.

Does Gemini Notebook hallucinate?

Yes, though less than general chatbots, and Google says so itself. Its help center states that “Gemini Notebook can make mistakes” and tells you to “always consult a qualified professional for medical, legal, or financial advice” (Gemini Notebook Help, 2026). In its 2023 launch post, Google wrote that source-grounding “does seem to reduce the risk” of hallucinations, and that it’s “always important to fact-check the AI’s responses against your original source material” (Google, 2023). Both lines apply to Gemini Notebook as much as to the original NotebookLM.

The clearest head-to-head test came in 2025 from researchers at Northwestern University and the University of Minnesota. In tests run in July 2025, Nick Hagar, Wilma Agustianto and Nicholas Diakopoulos asked ChatGPT, Gemini 2.5 Pro and NotebookLM reporting-style questions about a 300-document collection on US TikTok litigation. Across all 40 answers, 30% contained at least one hallucination. NotebookLM’s rate was about a third of the others (Hagar et al., arXiv, 2025). The authors credit its “RAG implementation with explicit citations” for the gap.

What counts as one? Here, a hallucination is a claim that contradicts your sources or asserts a specific fact they don’t support. That’s the definition a 2026 radiology study used when it scored Gemini Notebook’s Audio Overviews (Lee et al., Korean Journal of Radiology, 2026). Notice what it leaves out: whether the claim is true in the world.

What does “grounded” mean in Gemini Notebook?

Source grounding means Gemini Notebook writes its answers from the sources in your notebook rather than from what its model learned in training. Google’s chat help puts it plainly: “Chat responses in Gemini Notebook only use data from your sources” (Gemini Notebook Help, 2026). It also uses “direct quotes, text, and images from your sources as citations”, which Google says “help you check response accuracy”. Hover over a citation to read the full quote, or select it to jump to the passage in context. That’s the whole safety net, so it’s worth knowing exactly what it promises.

Grounding promises faithfulness, not truth. If a source says a policy started in 2019 and it really started in 2021, a perfectly grounded answer says 2019. Likewise, an opinion in a source stays an opinion only if the answer keeps it attributed. In other words, Gemini Notebook is as reliable as the pile you hand it, minus whatever it misreads. That’s why the checks later in this guide test the gap between a claim and its quote, not just whether a citation exists.

Where you ask also matters. Your notebooks also open in the Gemini app, and the two apps ground answers differently. In Gemini Notebook, “responses are grounded exclusively in your notebook sources”; in Gemini, they “may also include web search and other tools” (Gemini Notebook Help, 2026). So the same notebook can give a web-assisted answer in one app and a sources-only answer in the other.

Is Gemini Notebook a RAG system?

In effect, yes. Retrieval-augmented generation (RAG) is a design where a model first retrieves relevant passages from a set of documents, then writes its answer from them. The 2020 paper that named it found RAG models wrote “more specific, diverse and factual language” than a model working from memory alone (Lewis et al., arXiv, 2020). Google doesn’t publish Gemini Notebook’s architecture. However, its help center describes the same two moves: “When your notebook contains many sources, Gemini Notebook retrieves the most relevant information based on your question, then builds a response from it” (Gemini Notebook Help, 2026). Researchers who test it, including the Hagar and Tozuka teams below, call it a RAG system outright.

So every answer passes through three steps, and each one can slip:

1. Retrieve. It picks the passages that look most relevant to your question. A vague question can pull the wrong ones, which is why Google suggests making questions “more precise and understandable”.
2. Write. The model drafts an answer from those passages plus your chat history, which Google says is “used to generate responses.”
3. Cite. It attaches quotes from the passages it used. A citation shows where a point came from. It doesn’t prove the point says what the source says.

The retrieval step is also why grounding helps so much. In one 2026 study, NotebookLM, running on Gemini 2.0 Flash, staged 100 fictional pancreatic cancer cases with 70% accuracy. The same Gemini 2.0 Flash model, handed the same staging guideline directly, managed 38% (Johno et al., Radiological Physics and Technology, 2026). Same model, same document; the retrieval layer made the difference. For the wider tour of sources, chat and Studio, see how Gemini Notebook works.

What happens when your sources don’t have the answer?

It should decline. Google says that if “the answer isn’t in the source material, it won’t provide a response” (Gemini Notebook Help, 2026). Ask for something creative, such as rewriting the end of a short story, and you may see “Gemini Notebook can’t answer this question” (Gemini Notebook Help, 2026). Google lists two other reasons a question goes unanswered: a source that trips a safety flag, even in a historical context, and an unclear question in a notebook with many sources.

A refusal is the system working. The riskier case is the near miss, when your sources hold something close to the answer and the model stretches it to fit. For example, ask “What did the 2025 audit conclude about vendor risk?” of a notebook that holds only the 2024 audit. A refusal is the good outcome. A summary of the 2024 findings, worded as if they were current, is the bad one. That’s why the routine below always asks what the sources don’t say.

How often does Gemini Notebook hallucinate?

There’s no single rate. Published tests range from 13% of answers with a hallucination (Hagar et al., 2025) to 96% accuracy (Garcia-Font et al., 2026), depending on the task.

People clearly want one number, though. When we analyzed Google autocomplete on October 26, 2026, suggestions for this topic included “how often does notebooklm hallucinate”, “notebooklm hallucination rate” and “is notebooklm hallucination free”. Our own Search Console data shows the same curiosity: from September 28 to October 25, 2026, “does gemini notebook hallucinate” put one of our pages in Google results 35 times. Here’s what seven studies of NotebookLM found between 2024 and 2026.

Study	Task	NotebookLM result	Compared with
Hagar et al., arXiv preprint, 2025	Reporting questions over 300 documents	2 of 15 answers (13%) had a hallucination	ChatGPT 6 of 15 (40%), Gemini 2.5 Pro 4 of 10 (40%)
Tozuka et al., Japanese Journal of Radiology, 2025	Staging 100 fictional lung cancer cases from a guideline	86% accurate; found the right passage 95% of the time	GPT-4o 39% with the guideline, 25% without
Johno et al., Radiological Physics and Technology, 2026	Staging 100 fictional pancreatic cancer cases	70% accurate; 92% retrieval accuracy	Gemini 2.0 Flash 38% with the guideline, 35% without
Garcia-Font et al., Journal of Endodontics, 2026	46 yes-or-no clinical questions	96% accurate (44 of 46)	Gemini 89% (41 of 46)
Samaan et al., medRxiv preprint, 2026	Extracting 1,710 data cells from 57 papers	91.17% of cells correct; 4 major factual errors	Human reviewers’ consensus
Lee et al., Korean Journal of Radiology, 2026	Audio Overviews of 22 radiology papers	No hallucination in 56.8% of ratings; 0.93 per episode	A custom pipeline: 71.6%, 0.32 per episode
Beber et al., JPOSNA, 2026	Rating risk of bias in 27 observational studies	14.8% to 29.6% agreement with the reviewer	A human reviewer

Studies and preprints that tested NotebookLM directly (published 2024-2026), found through web, PubMed and arXiv searches on October 26, 2026.

Read across the rows and a pattern shows. Gemini Notebook does best when the job is to find and apply what a document says, such as staging a case by the guideline or answering yes or no from the literature. It does worse when the job needs judgment, like rating a study’s quality, which Beber’s team called “low reliability and accuracy”. It also slips when it compresses a lot into a few minutes of audio. Keep the scale in mind, too. Hagar’s whole test was 40 answers, and its authors say the findings “should be read as directional.” Two studies used fictional cases. Finally, most of these tests predate Gemini 3.5 in Gemini Notebook (Google, 2026), so they measured older models. Google’s own benchmarks for that upgrade are internal comparisons, so they aren’t in this table.

Where does Gemini Notebook get things wrong?

In five predictable places: reading too much into a source, handling numbers and long files, missing content it never imported, framing in Audio Overviews, and mixing weak or outdated sources with good ones. Knowing them tells you where to point your checks.

It reads more into a source than is there

The most common slip in the newsroom study wasn’t invention. It was “interpretive overreach”: answers that made “analytical claims the documents didn’t support”, such as confident guesses about who a document was written for (Hagar et al., 2025). The authors also saw “attribution drift”. In one case, a senator’s opinion from a hearing became an uncontested statement about TikTok’s dangers. Every sentence may sit next to a real citation; the slant is the error. Watch for verbs like “argues”, “proves” and “recommends”, and for any claim about intent or audience.

Numbers, tables and very long sources

Numbers fail quietly. In the radiology podcast study, one Audio Overview misquoted a standard deviation, and another said a dataset had 400 annotated cases when the paper said 250 (Lee et al., 2026). In the data-extraction preprint, most of the 151 flagged cells were omissions or incomplete extractions, and 11 landed in the wrong column of a multi-column table, mostly “where column domains overlap” (Samaan et al., 2026). Spreadsheets have a hard edge as well: Google Sheets sources are limited to 100k tokens (Gemini Notebook Help, 2026). Long files add a retrieval problem. A single source can run to 500,000 words, and the answer draws only on the passages retrieval picked. As a result, a 600-page report can hide the one paragraph that changes the answer.

Content that never made it into the notebook

Gemini Notebook can only ground answers in what it imported, and Google’s source help lists real gaps (Gemini Notebook Help, 2026):

• Web pages: “Only the text content” is scraped. Images, embedded videos and nested pages are left out, and paywalled pages aren’t supported.
• YouTube: “Only the text transcript of the video is imported”, so anything shown on screen but never said is invisible.
• Audio files: they’re transcribed at import, and low-quality audio may fail.
• Google Docs and Sheets: footnotes and comments aren’t imported.
• Images: Google warns that “certain types of images may not work as well.”

Google’s help pages don’t say how scanned PDFs are read. They do note, about Drive files, that a source “may appear differently in the Gemini Notebook viewer than the original file”. So open a scanned or table-heavy source in the source viewer before you trust answers about it. If the viewer shows garbled text or a missing table, the answers are built on that.

Audio Overviews add framing

Audio Overviews are the hardest output to check. Google says they’re “designed to be an objective reflection of your source content”, and also that they’re AI-generated and “may contain inaccuracies or audio glitches” (Gemini Notebook Help, 2026). In the radiology study, 50 of 88 ratings (56.8%) found an episode free of hallucinations. A smaller study of 8 episodes made from plastic surgery textbook chapters found “excellent accuracy with no extraneous insertion of information”. It also found “under- or overemphasis on topics that were emphasized in the source” (Taritsa et al., PRS Global Open, 2025). Two chatty hosts add their own glue, too: enthusiasm, analogies, “the big takeaway is”. None of it comes with a citation you can open mid-sentence.

Web sources and mixed notebooks

Fast Research and Deep Research find web pages for you, and once imported they’re sources like any other. You can import all results or review them first (Gemini Notebook Help, 2026), and Google doesn’t describe any quality check on what they bring back. A grounded answer that cites a weak blog post is still grounded. The agentic chat goes further, since it can search the web and run code. Google calls those functions “experimental and in early development” and warns that Gemini Notebook “can make mistakes and do unexpected things” (Gemini Notebook Help, 2026). Mixed notebooks blur things as well. Put last year’s policy next to this year’s, and both can be retrieved for one answer. Our guide to using Gemini Notebook for research covers scoping one notebook per question.

How do you check a Gemini Notebook answer?

Match the checks to the failure modes: open the evidence, make the model quote, and hunt for gaps. On an answer that matters, six steps take a few minutes.

1. Open every citation behind a number, a name or a strong verb. Hover to read the quote, then select it to see the passage in context (Gemini Notebook Help, 2026). Read the sentences around it, not just the highlight.
2. Ask for exact quotes. Have it set each claim next to the sentence that supports it. A claim with no quote is a claim to cut.
3. Ask what the sources don’t say. Gaps and hidden assumptions surface quickly when you ask for them directly.
4. Narrow the sources. Tick only the sources the question needs, and name them in your question. Google says naming sources “helps Gemini Notebook narrow its search” (Gemini Notebook Help, 2026).
5. Split huge sources. Upload a 600-page report as chapters, so retrieval has less to sift and each citation tells you which part to open.
6. Re-ask in a fresh chat. Chat history feeds every answer, so a new chat tests whether the answer holds up without earlier turns steering it.

Step 5 is our suggestion rather than Google’s, based on how retrieval works: fewer, smaller passages to choose from, and citations that point somewhere specific. For steps 2 and 3, paste these prompts after an answer you plan to reuse:

For each point in your last answer, quote the exact sentence from my sources that supports it and name the source. Mark any point that no source states directly as "not stated".

What does my question assume that these sources don't cover? List anything relevant the sources don't say, and any place where two sources disagree.

Which evidence in these sources argues against your answer? Quote it.

When an answer passes, keep it with its evidence. Save to note keeps “tables and clickable inline citations” inside the notebook (Gemini Notebook Help, 2026). Taking that trail out of the notebook is the part Google doesn’t cover.

When shouldn’t you rely on Gemini Notebook alone?

When a mistake costs more than the check. Google’s own line is to “always consult a qualified professional for medical, legal, or financial advice” (Gemini Notebook Help, 2026). The studies point the same way. Even the best clinical scores above, 86% to 96%, leave somewhere between 1 answer in 7 and 1 in 25 wrong. A human should read the source itself in these cases:

• A number or quote going into a report, filing or grade. Open the page it came from.
• Medical, legal and financial decisions. Use the notebook to find the passage, then have a qualified person read it.
• Judgment calls. Quality ratings and “which argument is stronger” are where the risk-of-bias study scored worst. Let it gather; you decide.
• Anything from an Audio Overview. Re-ask the claim in chat, where the citations open.

Sensitive documents raise a separate question, about who can see them. Our guide to whether Gemini Notebook is safe and private covers data use and training.

Can Claude or ChatGPT double-check Gemini Notebook’s answers?

Yes, if the answer reaches them with its citations attached. That’s the job of Kortex MCP, a connector we built that lets Claude, ChatGPT and other AI clients ask your notebooks. It’s included with Kortex Pro and Lifetime. Its ask_question tool returns Gemini Notebook’s answer with its citations, while ask_library asks up to three notebooks and keeps each answer separate with its own citations (Kortex MCP docs, 2026). Gemini Notebook still does the grounding; Kortex carries the result across as returned. A second model can then take on the tedious part of the routine: match each claim to its cited passage, flag claims without one, and point out where two notebooks disagree.

Ask my "Vendor contracts" notebook what the termination notice period is in each contract, with citations. Then check each claim against its cited passage and list any claim the passage doesn't fully support.

Be clear about what this is, though. We haven’t measured Gemini Notebook’s hallucination rate, and Kortex doesn’t change how it answers. The second model is a second reader, and it can misread too, so treat its flags as places to look rather than a verdict.

People do want this pattern. In the Kortex MCP waitlist survey (776 respondents, Sep-Oct 2026), 110 people asked to query their notebooks and get cited answers back in their assistant. One wanted to “retrieve grounded answers with source references”. Setup takes one URL, as our guide to connecting Gemini Notebook to Claude and ChatGPT shows.

In short, Gemini Notebook hallucinates less than a chatbot, mostly by overreading, and its citations make every slip checkable once you open them.

Frequently asked questions

Does NotebookLM hallucinate?

Yes, sometimes. Gemini Notebook (formerly NotebookLM) answers from your sources, which makes errors rarer than in a general chatbot, but not impossible. In a 2025 test over 300 documents, 2 of 15 NotebookLM answers (13%) contained a hallucination, against 40% for ChatGPT and Gemini.

What is the NotebookLM hallucination rate?

There isn’t one fixed rate. Studies from 2024 to 2026 report hallucinations in 13% of answers in a newsroom test, 86% to 96% accuracy on guideline and clinical questions, and hallucination-free Audio Overviews in 56.8% of ratings. Results depend on the task, and most samples are small.

Is NotebookLM a RAG system?

In effect, yes. Google says that when a notebook has many sources, Gemini Notebook retrieves the most relevant information for your question, then builds a response from it. That’s retrieval-augmented generation. Google doesn’t publish the architecture, but researchers who test it describe it as a RAG system.

Is Gemini Notebook hallucination-free?

No. Google’s help center says Gemini Notebook can make mistakes, and its 2023 launch post said source grounding “does seem to reduce the risk” of hallucinations while urging users to fact-check against the original sources. Grounded means faithful to your sources, not guaranteed true.

Can Gemini Notebook answer from outside my sources?

Standard chat answers only from your sources and won’t respond when the answer isn’t there. There are exceptions: the experimental agentic chat can search the web, and notebooks opened in the Gemini app may use web search. Web pages you import through Fast Research or Deep Research become sources like any upload.

Do Audio Overviews hallucinate?

They can. Google says Audio Overviews may contain inaccuracies. In a 2026 study of episodes made from 22 radiology papers, 56.8% of radiologist ratings found no hallucination, with 0.93 hallucinations per episode on average. Check any number in the notebook chat, where the citations open.

---

Editorial note: fact-checked against the official help pages and docs linked above on October 26, 2026. Spotted something out of date? Contact us and we will fix it, or read more about Kortex.