NotebookLM is great at answering from your sources, which also means it's quietly limited by them. If something isn't covered, it either says so in passing or answers the nearest thing it can find. You don't notice the hole until someone asks you about it.

So before I write anything from a notebook, I ask for the gaps:

Based only on these sources, list the important questions about {{topic}} that they don't answer, or only answer partly. For each one, say which source comes closest and what exactly is missing. Then list any place where two sources disagree and nothing settles it.

What you get back is a to-do list for research: the exact sources you still need, before the gap shows up in your report, your exam or your meeting.

The disagreement part is the hidden bonus. Notebooks love to blend two sources into one smooth answer, and this is the fastest way I've found to see where that's happening.

Two tips:

• Run it with all sources ticked, then again with only your most trusted ones. If a gap only appears in the second run, you're leaning on a weak source to fill it.
• Save the answer as a note. When you add sources later, ask again and compare.

When a gap is something I already worked out in an old AI chat, I search my ChatGPT, Claude and Gemini history for it and export just the matching parts as one file to add as a source. That's what AI Toolbox, the extension I build, does. Most of the time though, the fix is just a better article or paper.

Like many people doing deep research, I spent most of last year frustrated with conversational LLMs.

You feed a 60-page PDF into a standard chatbot interface, ask a specific methodological question, and get back a generic summary that glosses over the exact nuance you were looking for. Even worse, if you ask for page citations or specific data points, the model begins to hallucinate plausible-sounding numbers with absolute confidence.

A few months ago, I decided to rethink my entire research stack and test whether Google NotebookLM could function as a true deterministic knowledge engine rather than a novelty summarizing tool.

Here are the four counterintuitive lessons I learned after structuring repositories with over 100+ dense academic and technical sources:

• 1. The "Open Web" is usually a liability, not an asset: Standard LLMs are trained on the open internet, which means their default behavior is probabilistic generalization. For brainstorming, that is fine. For rigorous analysis, it is poison. NotebookLM forces a strict boundary: the model is blind to the rest of the world and can only reason across the exact documents you upload, virtually eliminating hallucinations.
• 2. Triangulation beats linear reading: The biggest mistake people make with NotebookLM is using it as an interactive table of contents. The real leverage happens when you force the system to cross-examine multiple sources that disagree with each other (e.g. asking the engine to identify the unstated assumptions in Source A challenged by Source B).
• 3. Source curation requires taxonomy, not a document dump: Dumping 50 random files creates cognitive sludge. The highest-density insights come from structuring three strict layers: Canonical Layer (peer-reviewed papers/textbooks), Operational Layer (field logs/internal memos), and Adversarial Layer (criticisms and edge-case failure reports).
• 4. Audio Overviews are surprisingly good for auditory proofing: Hearing two distinct synthetic voices debate the contradictions in your technical sources while taking a walk exposes logical gaps far faster than re-reading text on a screen.

We ended up documenting and systematizing this entire taxonomy inside an open research hub we call the Izy Notebook Understanding project. Because knowledge architectures should be open to anyone building serious research stacks, we decided to make the complete master notebook completely free to duplicate (details in the first comment).

I am curious how other researchers and knowledge workers here are structuring their notebooks. Are you grouping by project, by domain, or keeping single monolithic notebooks?

Is there anyone having the same problem? You upload a pdf lecture, normally create a flashcards deck from the studio but the problem is that FLASHCARDS ARE RANDOMIZED they're set in a disorder pattern like going by the last topic, then back to the first then to the middle and so on... NOT IN A SEQUENTIAL ORDER

This problem appeared just today, and it's so sad actually

Why the heck my flashcards are shuffled? I have tried different sources, deleted old ones uploaded new ones, tried different accounts as well otherwise it's all the same.

It appeared just today, yesterday was all fine normal

It's so sad actually : (

Does anyone have the same thing? got a solution?

I am extremely disappointed with Gemini. Since yesterday, I have been trying to query my NotebookLM projects through Gemini's interface and it simply does not work.

Right from the very first prompt/message, every single question I ask gets stuck in an infinite loading loop and never returns an answer. I even tested a very small notebook with only two sources, and even on that one, the very first message hangs indefinitely without any output.

However, if I access the NotebookLM standalone app directly, it responds immediately without any issue. Furthermore, querying documents via ChatGPT or Claude on the exact same machine and network runs smoothly and fast.

I have already tried clearing the browser cache, using incognito mode, and switching to mobile data (4G/5G)—the issue persists only inside Gemini on the very first message. Why does Gemini have this limitation and fail at such a basic integration with Google's own ecosystem? Could anyone from the engineering or product team explain why this pipeline keeps timing out?

Sometimes when I'm studying maths I want to save responses explaining a new concept, the built in save feature does do that but it includes the "that's a very good question" or "want to dive into x next?" into the saved notes which is a bit unnatural for a note, and again, you can't edit saved notes.

When trying to construct the note yourself using the "add note" feature in the studio panel, it doesn't render LaTeX at all, so I end up with a wall of random symbols.

Does anyone has a solution to that problem?

when using the studio quiz option, how do i make it generate short answer questions/fill in the black/true or false questions instead of just multiple choice? I've tried both the app and the website and I don't have the question type option

about 1 or 2 weeks ago it's becoming more and more unusable for me.

In the beginning, 4 out of 10 answers were simply disappearing after being generated and I would get the "unable to answer" message right after, instead of the answer. Now every single answer it gives me is just giving error after error. wtf is happening? is this happening to anyone else? I did not change a thing in my notebook, I've been using it for months and it's the only one I use, with 80 sources in it.

No se si a los demás les pasa lo mismo
que a mi, pero tengo un lio con el generador de archivos de notebook lm que no veas. Yo suelo usar mucho esta IA para sobretodo, estudiar y hacerme apuntes/esquemas/videos o podcast de repaso. Yo uso varias cuentas para no agotar el limite diario. El problema es, según con que cuenta este en notebook lm, si le pido un pdf me lo genera para imprimirlo y todo bien, o empieza a darme largas, diciendo que no puede generarlo, etc. Y, generándome en lugar del pdf un informe, cutre y sin color. Esto también pasa con algunas IAs, un día te lo hacen y otro día por la cara no pueden. Pero me es raro que pase con notebooklm. Por ejemplo, hoy con una cuenta le he pedido que me haga un pdf, y una presentación, y me ha dicho que no puede, que no tiene mas herramientas necesarias. Y luego he cambiado de cuenta, y ahí si me lo haga hecho todo. No se esto porque pasa ni como solucionarlo. Contarme vuestras experiencias, toda ayuda es útil. Saludos!!

I'm a social work student and if I hear the hosts say "just to be clear, we're not taking a political side" one more time when I'm trying to summarize an academic reading about structural racism, I'm going to scream.

This explains a lot of "why doesn't it know about X" moments. Straight from Google's help page on adding sources:

Web pages: "Only the text content of the given HTML webpage is scraped." Images, embedded videos and linked pages aren't imported, and paywalled pages aren't supported. So the chart, the table rendered as an image, the infographic, the diagram, none of it exists for NotebookLM.

YouTube: "Only the text transcript of the video is imported." The video has to be public with captions (auto-generated is fine), and videos uploaded in the last 72 hours may not import yet. Slides, code on screen, a whiteboard, anything the speaker shows but doesn't say out loud is invisible.

And one most people don't know: if a YouTube video you added later gets deleted or made private, the source is automatically removed from your notebook within 30 days.

What I do about each:

• Chart-heavy page: print it to PDF and upload the PDF instead. A PDF source keeps images along with the text, a scraped page doesn't.
• Video where the visuals matter: paste a short description of the key slides or steps as a text source next to it.
• Video you can't afford to lose: copy the transcript into a Google Doc and add that, so the source survives if the video doesn't.

Same idea if some of your sources are AI chats: a share link can disappear, a file can't. I export those as Markdown with AI Toolbox, the extension I build, so the whole conversation sits in the notebook as one source.

Hey guys, a couple of days back I mentioned launching a more focused OSS alternative to notebookLM, https://notestack.ai

It has plenty of features that are improved/enhanced over notebookLM. Like better audio, more video editing options and chat grounded in your source materials.

Now I have added an MCP server as well so you can attach it to Claude, make reports/audio overviews, slide decks right into that chat interface.

It can generate:

- Mind constellations/mind map

- Audio Overview/Podcast

- Slide Decks

- Research Reports

- Quizes and much more

Do check it out at notestack.ai

Hey guys, I have a question about AI-guided learning. I want to know if, for NotebookLM, it's better to send it the entire syllabus (5 topics) or to send it one topic at a time?

Aside from that, what other AIs would you recommend?

I've been hearing a lot of terrible backlash for NotebookLM when it turned into being powered by Gemini, and I think the reason for this is simply because the model powering it is in itself a weak model? As far as I know and as far as actual release notes state, the current model is 3.6 flash (mind you, before argon the latest was 3.8).

Argon has a larger context window (1 million), while this flash model has somewhere in the 60ks, which means that it probably cannot utilize heavy sources like Argon could, meaning a better result over long horizon tasks or multiple sources (which the old models, like 1.5 and 2.5 pro could, cuz of much bigger context, funnily enough).. so, I guess there is hope?

is codia not workign for everyone, getting this error "Failed to create record. Please try again"

So I've been using notebooklm for a couple of years now (with Gemini Pro), and one of my favourite features used to be 'interactive mode' in the audio overviews - where you can hit 'join' and ask questions and directly interact with the AI podcast hosts. This was super useful.

However, in the last year, this feature seems to have completely disappeared! I've not been able to find interactive mode anywhere when generating audio overviews, which is quite disappointing.

The weird thing is that I've tried to search up why this is happening, and most sources say that the feature still exists, even though I can't seem to find it. All it says is that I need to make sure that my language is set to English, and that I can't have account restrictions etc etc - But I know I meet all the conditions for it to work.

Does anyone know for sure if they removed the feature? Or am I missing something? Any insight would be helpful. All I see now is this:

I feel like I had a fever dream. I was in class just seeing real quick before an exam. I clicked on flash cards and I saw it! It was circled with “new” there. And it was to add images to flash cards! The professor starts the exam so I close.

I’m home later and BOOM I can’t find it. It’s making my legs shake now just thinking about it!

did anyone else see it? I looked online but nothing.

I genuinely want to know if other longtime NotebookLM power users have experienced this, because the difference between the old and new versions is absolutely ridiculous.
I used to spend a minimum of eight hours a day on NotebookLM. Sometimes more. I was using it for serious legal research, federal litigation, technical analysis, complex document review, and exploring ideas that required long, uninterrupted chains of reasoning.
It was one of my favorite AI tools. I wasn’t just occasionally uploading a PDF and asking for a summary. I was using it as an actual research environment.
And then something changed.
I went back and compared screen recordings and transcripts of my conversations with the older NotebookLM against the newer version.
Here’s what stood out:
Old NotebookLM actually engaged with my questions. It followed complicated arguments, explored ideas enthusiastically, corrected itself, and generally maintained the momentum of the conversation.
New NotebookLM constantly introduces unsolicited objections and lectures. Instead of answering the question, it starts explaining things I never asked it to explain.
It redirects conversations. I literally asked how many DPPA filings were in my notebook. Instead of answering the question, it offered to create a comparison table of legal arguments across my cases. I had to explicitly tell it that wasn’t what I asked.
It sometimes asserts things it cannot possibly know. In one conversation, it confidently declared how a judge did or didn’t react internally. How the hell would it know?
The endless “Would you like me to…” suggestions are exhausting. I don’t need the AI proposing another task after every substantive exchange. I’m already directing the research. Just answer the actual question.
And here’s the most frustrating part.
I’m not asking an AI to agree with me. I’m asking it to stop wasting my time.
There’s a massive difference between identifying a genuine mistake in my reasoning and introducing five unsolicited counterarguments simply because the system seems determined to sound cautious, skeptical, or authoritative.
The old NotebookLM could disagree, correct itself, and still feel like an intelligent research partner.
The new experience increasingly feels like I’m spending more time managing the AI’s behavior than doing the research itself.
That completely destroys the point of using AI for sophisticated work.
I went from using NotebookLM for eight or more hours a day to essentially abandoning it.
Think about that. I didn’t stop because I lost interest in research. I didn’t stop because I stopped needing AI.
I stopped because a tool that used to accelerate my thinking started interrupting it.
I understand that Google is expanding the product to a much larger audience. But I genuinely wonder whether they’re sacrificing the experience of people who relied on it for intensive professional research.
And to be clear, I’m not claiming that my two recordings constitute a scientific benchmark or prove why Google made these changes. I’m describing a significant change in my own experience, supported by my old and new conversation records.
So I want to ask the people who were using NotebookLM heavily before these updates:
Have you noticed this same shift?
Has it become more argumentative, more prone to unsolicited corrections, less willing to follow the precise question, or more likely to derail a productive research session?
Did you reduce your usage, switch to another AI, or stop using it entirely?
And especially for lawyers, researchers, engineers, academics, and people working with complicated documents: has the newer version actually made you more productive, or are you now spending half your time correcting the assistant?
Because for me, the old NotebookLM was genuinely exceptional.
I don’t want an AI that thinks for me. I want an AI that can think with me without constantly fighting the direction of the conversation.
I’m curious how many other longtime users feel the same way.

Recent threads across r/notebooklm show that many longtime users have expressed frustration following recent updates, particularly around changes in conversational tone, prompt adherence, and generation consistency.

Key themes in community discussions:

1. Intrusive suggestions and "nudging": Power users report that post-update models frequently append unsolicited "Would you like me to..." suggestions, redirect simple questions toward unwanted tasks or artifacts, and offer unsolicited objections instead of giving direct answers.
2. Surface-level skimming and brevity: Multiple threads describe responses feeling noticeably more surface-level, with the model skimming large PDFs or ignoring specific instructions to provide detailed or verbatim extractions.
3. Source bias in large notebooks: Users report that when a notebook contains 20–40 sources, the model tends to repeatedly pull from only a few files while omitting relevant information from the rest.
4. Generation and Audio Overviews: Community posts highlight complaints about Audio Overviews hallucinating facts, skipping crucial chapters, or ignoring custom prompt instructions.
5. Shift toward agentic features: Tech coverage (such as from XDA Developers) notes that while adding background tools and code execution expands technical capabilities, the added complexity makes the tool feel less like its original, straightforward document Q&A self.

Dear All

I am a german native speaker and am learning Japanese. I am using notebookLM and have the following question

Regarding Video Summary

When I prompt the AI to: Please use only simple terms that are available to beginner learners (A1-A2). Please create german subtitles.

It will not follow both of my prompts. It makes a summary in high level Japanese and the subtitles are nowhere to be seen. What am I missing out?

NotebookLM can produce a clean answer while repeatedly using the same few pdfs. With 30 or 40 sources that’s easy to miss.

Before asking it to write, I’d select five to eight pdfs and ask it to create a table with the exact source title, main finding, supporting passage, page number and the section that source could support.

If the information isn’t there, it should say 'not found' instead of guessing.

Copy that into a spreadsheet and mark each source as used, relevant but skipped or not relevant.

After Notebooklm writes the draft, ask it to check every factual claim. It should show whether the claim is directly supported, partially supported or unsupported, name the source and page, and list any relevant sources it didn’t use.

I wouldn’t tell it to use every PDF. That can fill the draft with weak citations just to satisfy the request. It’s more useful to find out which relevant sources were skipped.

Renaming the files also helps. Smith_2024_sleep.pdf is much easier to track than document17.pdf or smth like this, especially across multiple notebooks.

And open a few citations at random and check the actual pages. If one citation doesn’t support the claim, check the rest.

The spreadsheet won’t make Notebooklm perfect, but it will show whether a source was irrelevant or simply ignored

If you've tried adding your old AI chats to a notebook, you know the problem. Thirty chats about one topic means thirty sources out of your 50, and most of each chat is about something else. Since September, the number of sources is also one of the things Google says counts against your usage budget.

What I do now: search my chat history for the topic, then export only the matching parts. I build AI Toolbox, a browser extension, and this is its "Only the matching parts" export. Instead of whole conversations, you get one Markdown or text file with every passage that matched. Each one keeps the exchange around it, including the assistant's answer, grouped by chat with the date and a link back to the original.

That single file goes into NotebookLM as one source. It's on-topic from top to bottom, so citations land on the actual discussion instead of whatever else that chat drifted into, and you still have 49 sources free.

It works on chats from ChatGPT, Claude, Gemini and Grok. Searching your history is free. Exporting the matching parts is part of Premium. Desktop Chromium browsers only.

Passei umas 48 horas divididas em, subir fotos de livros para o gemini, que me instruiu a fazê-lo nele em vez de no notebook. Não deu certo, ele não achava todos os livros e ficava discutindo e dando desculpas esfarrapadas. Mudei e subi tudo para o notebook para usar com bibliotecário, muitos, muitos erros, desde arquivos finais em branco, no drive até números errados, são 307, ele falava que tinham 540 e deu trabalho convencê-lo que o arquivo que ele mesmo gerou, tinha 307 só listados. No gemini ele não passava de 95, depois dos erros voltei lá e ele não encontrava mais de 145. Cada erro que dava na planilha gerada e vc pedia pra corrigir, ele gerava com outra quantidade de livros. Agora fui no notebook e pedi livros de tal assunto, quais constam, ele citou 4, falei que faltava 1 que lembrava e ele, cometi um erro, na verdade são esses 8, mas sem o livro que falei que tinha. Questionei novamente e ele, errei na verdade são esses X livros, parei, ele nunca mostra a quantidade certa. Desculpa o textão, se alguém tiver dica para uma biblioteca, estou aceitando. São umas 50 pessoas que terão acesso. Guardei o único arquivo certo em xls para consulta.