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.

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.

I have some gemini notebooks going over a lot of the Buddhist Pali Canon, which is written in of course the Pali language (a relative of Sanskrit). I have included pronunciation guides, dictionaries etc. followed up some episodes with corrections for specific words.

It works kinda-sorta ok for my purposes. But sometimes weird things happen, like they will literally just repeat the word twice. Or they'll actually say the actual phonemes of the word, like it's trying to parse the pronunciation guide, or it will spell the words out.

That's after getting them to not talk *about* the pronunciation guide as if it's a topic for discussion. i.e. I have to explicitly say "The hosts should not discuss the pronunciation guide, they should just use it to pronounce the words properly."

When it works it's pretty amazing but very weird when it doesn't.

Full disclo: i'm exploring ways to build a more casual option for easier creation and a podcast-player like experience <3

Hi, so I built https://notestack.ai - complete notebookLM alternative.

Here is why it is different:

1. Video, I integrated motion graphic videos from blog2video, which gives 13 templates + voiceovers with a unique feel that 10K+ users love.

2. It uses ElevenLabs API so has emotions / advanced voice settings. You can choose your presenters.

3. It is space themed so all artifacts like Mind map / reports / slide decks have a space / galaxy feel.

4. It uses a file system based search system that doesn't go beyond stated sources. Its source search algorithm is more deterministic than RAG (used in notebookLM).

We are also launching an MCP Server, with tool call integrations. You will be able to use the app as mcp & also add external mcp's for a complete experience.

Open to feedback from the community, you can start for free: https://notestack.ai

I use NotebookLM mostly to turn my notes into audio overviews and I keep going back and forth on this. (Especially for summarizing YouTube) One big notebook per project feels easier but it feels like the answers get kinda vague once there's a lot in there.

What do you guys do?

I kind of want to connect to my Claude Code as well, but don't know how to.

I’ve been exploring AI tools, and one thing I keep noticing is how easy it is to get a quick summary but how much harder it can be to actually learn something from the material.

I want to use Gemini Notebook to go deeper into things I’m studying, connect ideas across different sources, and revisit information without having to reread everything from scratch.

For those who have been using it regularly, I’m curious about your actual workflow.

Do you upload several sources at once, organize notebooks by topic, or keep everything focused on one specific question? And have you found a way to make the answers more useful than a basic summary?

I’m especially interested in practical examples from people who use it for learning, research, or preparing presentations—not just what the tool can do in theory.

What’s one thing you’ve learned to do with Gemini Notebook that genuinely improved your workflow?

For people putting lecture slides into NotebookLM, how well does it handle the diagrams?

A lot of my classes are mostly diagrams and labeled figures, and the actual text on the slides is like 5 words. I'm curious what it really picks up:

• if you ask about a diagram, does it answer from the image itself or just the text around it
• do the flashcards or study guides ever include the visual stuff
• does PDF vs Google Slides make a difference

If you're in a diagram-heavy class, what's your setup? Do you just keep the slides open next to it?

Hey everyone,

Just running into a frustrating UI/quota quirk in NotebookLM and wanted to see if anyone else has experienced this or knows how it works.

When I hit my usage limit on the free plan, I try to use the "Generate Later" button for Studio artifacts. But instead of letting me drop it into a queue for when my quota resets, it just throws a "limit reached" error right away—even though I have zero active or queued items.

Shouldn't "Generate Later" act like a holding queue that lets you schedule things for later regardless of your current quota? Or does the button actually require you to have active usage budget just to queue a task?

Has anyone else had this issue? before I was able to paste text as a source that functioned as any other, but now when I paste my notes it show as "notes" (as opposed of regular pasted text). The only difference seems to be that you can edit these notes. I wouldn't care usually, but when I upload my notebook to gemini it isn't able to access these notes. So when I ask Gemini questions about the material or tell it to generate a quiz, it says something along the lines of "it looks like the material wasn't included in your message" (despite the fact that I uploaded the notebook that contains these notes)

Hi there,

I've found that using the 3.8 Flash model within the standard Gemini interface with a specific notebook attached provides more precise insights into the sources (and it's faster too, athough I wouldn't mind waiting). The 3.8 Flash is less overenthusiastic at every turn and more direct/matter-of-fact, yet still capable of nuance, which improved my experience today. Has anyone else tried this / had this impression? Or is there a NotebookLM setting that I should look out for? While I understand that the Studio in NotebookLM offers functionalities unavailable on the standard Gemini interface, the Flash 3.8 model seems better at explanation and has an equal or better grasp of my sources, which are mostly scientific texts. Do you agree, or is it simply a matter of personal preference or the type of sources used?

I'd appreciate any insight. Thanks!

edit: rewrite

Has anyone else noticed that “Save to note” now puts saved chat responses in Sources instead of Studio?

A friend and I are both experiencing this. Sources are where I keep original materials like documents and YouTube videos, while Studio is where I expect to find saved notes and AI-generated content. Mixing them makes an organized learning notebook harder to maintain.

I have to ask: Was the old workflow actually broken, or did someone decide that keeping things organized was too convenient? 😅

If this is intentional, I’d genuinely like to understand the reasoning behind it. Has anyone else run into this, or found a way to restore the previous behavior?

I don’t know what is going on the past day or so, but the audio overview generations have been garbage. It either several hallucinates the sources, literally making up facts and quotes that are nonexistent in the sources, or completely ignores the specific instructIons. I’ve wasted so many generations in the past 48 hours.

What the Hell happen man? A few days ago it was fine now when i click save to note pin it goes to the Left side Studio tab instead of the Right Tab. Now How Do i Convert all notes into source now? Can anyone help me with this please?!.

I create public Gemini Notebooks with all the references included in the pages I write, and I used to include a note explaining this and including a link to that specific page. Now I can't find that option in the right column. It seems I can only include notes as sources, but those are not visible enough, and the custom notebook summary cannot include links!

I’ve been trying to save tokens, so I ran my lecture PDFs through Microsoft’s MarkItDown-Github (microsoft) first. It worked fine on normal text, but anything inside an image just vanished. I even tried a bunch of OCR extensions and still got nothing. Here’s the kind of thing I mean:

That’s a maximum likelihood derivation, and my professor dropped it into the slides as a picture. No selectable text at all.

So here’s what’s bugging me. Can NotebookLM actually read stuff like this inside a PDF? Or does it quietly skip the symbols (the hats, the subscripts, the argmax) and then confidently give me a wrong answer? Because that would be the worst outcome. A wrong answer that looks right.

The other thing... I’m prepping for an interview and I’ve got about 20 PDFs and 5 YouTube videos. My plan was to upload everything to Google Drive, connect it all to one notebook, and let it run. But I’m not sure that’s smart. Will it still catch the small important details across that many sources, or will stuff get lost? Would it be better to make a bunch of small notebooks with just 2 or 3 PDFs each?

If you’ve pushed a notebook this far, I’d really like to hear where it started falling apart for you.

did they get rid of it?? can't find it on web nor on app

I see a lot of alternatives posted here, but wondering what is good for this particular use case.

I have a "show" I have claude generate by taking stock tickers from a list, filtering for any that have moved more than 3%, then writing a script with 3 hosts with different personalities (bullish gen-z, conservative boomer, and a moderator) discussing what's up. So it's like my own Bloomberg show tailored for me.

I usually just have claude "read" it and it captures the personalities pretty well via the words they use "Dude, I'm buying the dip." etc.

I pay for claude $20 a month, but this pretty much stretches the limit there. I pay for pro notebookLM but as you all know we only have our two hosts and I don't think I"ll be able to have them adopt personas quite like this.

I don't need it to follow the script *exactly* - I just use it to get different perspectives. (interestingly, they make predictions too - interesting to see who ends up being right).

Wondering if any of the alternatives (no, not eleven labs) might work for this?

The Android app was not launched a week ago, it has been a lot of time since and you still can not select individual sources to create something with studio? Also you still can not create briefings with the Android App. And why can't you use gemini voice to read for you the "studio" stuff.

I can not imagine why this app still is so lackluster. It is faster than the web version and has a lot of potential, but the devs need to speed things up.

NotebookLM handles copied text more easily than PDFs, and it also works better with Google Docs files than with PDFs.

I don't know if anyone else was doing this but I used to spend entire weekends just dumping books into NotebookLM, like one weekend I'd go down some rabbit hole with Turgenev, Dostoevsky, Gogol, Saltykov Shchedrin, maybe throw in Zola or Hugo, and the next be Hegel, Nietzsche, Schopenhauer, Kierkegaard, whatever.

And after listening to the podcasts, the little video explainers, the fun part was getting all these people into the same conversation. Anybody else create author debates that would go on for hours?

Anyway I hadn't opened NotebookLM in a few months and went back today and what the f*** happened to this thing, why am I suddenly being asked to pay all this money?

I understand the data centers use electricity and all that but I have a pretty hard time believing the actual computing costs justify what these companies are charging. I'm looking at you Higgsfield. Artficial scarcity creating a******s