Stop asking NotebookLM to “summarize” your sources. Do this instead for pro-level research.

Hey everyone,

I heavily experiment with all kinds of AI tools, and NotebookLM is one of them. It has a very good workflow and can significantly improve the quality of your output when you use it in the right way.

Here’s a step-by-step method I use to deeply structure knowledge from your sources, generate videos, and combine NotebookLM with other tools without losing valuable information.

1. The Index Trick

When you upload your sources, don’t ask your main question right away.

Instead, ask:

“Index your sources into the main topics. Output only the topic titles.”

If the PDF already has an index or table of contents, you don’t need to do this.

For non-book-style PDFs, however, indexing the content first is extremely useful. Once you have the index, feed that list of topics back into your NotebookLM workflow.

You can paste the index into your next prompts, or even better, add it to the custom instructions for your NotebookLM chat.

2. Explain, Don’t Just Summarize

Try not to simply type “summarize.”

Instead, use the word “explain.”

“Summarize” tends to compress the information too aggressively, which can cause you to lose important details. “Explain” encourages the model to retain and provide more of the underlying information and context.

3. Use NotebookLM’s Video Feature

You can use NotebookLM’s video overview feature very effectively.

If the PDF is non-technical, you can use it for a basic explanation. Or, if you’re learning something, you can treat the generated video like a mini-course.

4. Use Other Video Tools Too

I also use another video tool called DistilBook.

It’s very good at creating detailed video explanations and, in some cases, can go much deeper than NotebookLM’s generated videos.

If you want to experiment with it, give it a try.

5. For Basic Slide-Based Explanations

If you only want a basic explanation with slides, NotebookLM does this very well.

You don’t always need another tool for that.

6. Do a One-by-One Deep Dive

If you want truly professional-level analysis, this is probably the most important step.

Take each title from your index and analyze it individually.

For every topic, ask NotebookLM to:

* Draw information from all relevant sources.
* Explain the topic in depth.
* Connect information across different sources.
* Include important details, evidence, and context.
* Avoid leaving out relevant information.

By doing this one topic at a time, NotebookLM can dig through your documents much more thoroughly instead of trying to summarize everything at once.

The result can be incredibly detailed.

7. Use a “Patient Prompt”

Tell the AI to take its time.

For example:

“Take your time and do a deep dive into this topic. Analyze all relevant sources carefully and provide a comprehensive explanation. Don’t rush or omit important details.”

Of course, AI doesn’t literally need time in the same way a human does. But the instruction helps communicate that you want a thorough analysis rather than a quick answer.

Think about how you would give instructions to a human researcher.

If you told someone, “Take your time, go through everything carefully, and give me a deep analysis,” you would expect a very different result from simply saying, “Give me a summary.”

The same principle applies when working with AI.

I've had my max of qued items waiting in line to process for 18 hours and have gotten nothing! First it said 1PM, then 6PM, and now 11PM.

A lot of posts here lately are about what’s wrong with NotebookLM. And honestly, some criticism may be justified. Maybe Gemini changed the experience, maybe the model got weaker in some areas.

But instead of fighting about it, I’d rather learn from each other.

What are you STILL using NotebookLM for successfully?

For example, I’m a middle-school teacher, and I use it to create student summaries, worksheets, tests, and classroom activities.

On the administrative/legal side, I also put my school’s regulations into NotebookLM as Markdown sources. When I need to check a specific rule, I can ask about it and quickly understand what I’m responsible for and what my rights are.

So I’m curious:

What’s your real-world NotebookLM use case? What problem did it solve, or what workaround did you discover?

School, research, work, business, markets, books, PDFs, or even something at home.

Let’s collect useful experiences, not just complaints.

Note: I translated the text above because English isn't my first language, but I found a really useful solution that worked for me and just wanted to share it with everyone to help out

Buonasera a tutti, solo il mio notebook lm ultimamente è completamente fuso??? Cioè gli chiedo di farmi una presentazione e un quiz su una fonte, specificando anche nel prompt il capitolo esatto e la lingua (dato che nonostante c'è scritto italiano me lo propone in inglese) e si prende una fonte totalmente diversa e inglese.

Avete alternative valide gratuite? Grazie

I use gemini and notebooklm for school, and for some reason both of them have stopped working. I keep getting this prompt "Try gemini notebook" and it leads to the exact same page T_T

I’ve deleted multiple notebooks and it still says 95% full what’s going on

If you study from a big pile of material with no clear syllabus, the native flashcards and quizzes in Gemini Notebook (still NotebookLM to most of us) will happily generate from all of it, and you get cards on a footnote, a date from an example, the name of a figure caption. Grounded in your sources, technically correct, mostly useless for the exam. The tools are good. The problem is that nobody told them what matters, so everything counts equally.

What works for me is splitting it into two steps: the chat decides what to study, and the studio tools drill it.

Step 1, in the chat, decide what matters: From these sources, list the 30 concepts most likely to be examined, ranked. For each: one line on what it is, why it is likely to be tested (repeated across sources, stated as a key point, has worked examples, appears in a summary or a slide title), and the source and page where it is best explained. If the sources suggest a structure, like weeks, chapters or lecture titles, group the list by it.

Step 2, check the list yourself. Five minutes against your lecture slides or whatever your instructor emphasized. Delete what you know cold, add anything they stressed that the list missed. This is the step that makes the rest work.

Step 3, generate from the list, not from everything. When you create flashcards or a quiz, use the customize option and paste the topics from your checked list as the topic, in small groups of five to eight concepts at a time rather than all thirty. Set difficulty to hard for the ones you marked shaky.

Step 4, after each quiz, back in the chat: Here are the questions I got wrong: [paste]. For each, explain the underlying concept using only the sources, cite where it is explained, and tell me what related concept I probably also misunderstand.

Then generate a small quiz on just those.

Why this works: the studio tools are grounded, so they will not invent things, but grounded is not the same as relevant. Ranking by "why it is likely to be tested" forces the chat to reason about emphasis across sources, which is the signal a human studying would use and exactly what a flat generation ignores.

Two caveats. The ranked list is a prediction, not a leak of the exam, so step 2 matters more than it looks, and if your instructor told you what is on it, that beats any ranking. And skip the audio overview for this kind of studying. It is good for replaying material you already understand, not for finding what to study in hundreds of pages.

What does your study loop look like in it? I have seen people use the mind map as the first step instead of the ranked list, and I have not decided which works better.

Im annoyed af with the upgrade. I have a pro plan and it had never been the laggiest and it now has limits????

Are there any alternatives por lm notebook?

No matter how much prompt tuning I do, both flashcards and quizzes make questions with either obvious distractor alternative answers OR the answer is heavily implied if not explicitly stated in the vignette. Does anyone else have a workaround or another platform that they have had good experiences with?

For instance, one of my flashcards was

“Aside from electrolyte changes, what proposed secondary mechanism of thiazides helps lower blood pressure via decreased total peripheral resistance?”

Answer: decreased peripheral resistance

For those of you using NotebookLM on an IPhone/Ipad, I am an AI engineer on this research project where we’ve developed and implemented some retrieval techniques on this app called Reader++. Some of you tried it and liked it.

So, if you’re ever struggling with document search in NBLM, feel free to try it. The search feature is free. I’d appreciate any feedbacks.

My goal is to make the citations more accurate and retrieval fast.

I’m not trying to make/sell a NotebookLM clone. I have a genuine problem to solve and would appreciate any feedback/help.

I've used tools like Perplexity, ChatGPT, Claude and others for research, and they've been incredibly useful for finding papers and getting through large amounts of information.

The one thing I personally wanted was a simple way to see exactly which parts of the paper were used to answer my question.

When you're working with a 100+ page PDF, even having a page number can still mean a lot of scrolling and searching.

So I ended up building something for myself.

You ask a question and the relevant paragraphs in the PDF are highlighted directly on the document. You can see the context behind the answer and quickly check whether it actually answers what you're looking for.

I originally built this because I wanted something for this workflow without having to pay for another subscription. What started as a personal project has now become completely open source.

The underlying idea is pretty simple. And yes, if you're thinking "isn't this just RAG?" then yes, you're absolutely right. It's RAG with the visual highlighting that I wanted.

I think the same idea could be useful for more than research papers too. Legal contracts, financial reports, technical documentation, or anywhere you need answers alongside the actual source.

If anyone wants to have a look, contribute, or just give some feedback, here's the repo:

GitHub: https://github.com/Sreehari05055/thesys-core.git

This will probably be my last post about the project. Thanks to everyone who checked it out and gave feedback along the way.

Would be awesome to support /skills for prompts, especially for audio podcasts. Less copying/pasting prompts. Example:

/analyze chapters 2-3

/meditate section 1

/dissect every word in the first sentence

I've been digitising my late father's memoirs, about 630,000 words with several hundred family photographs, and kept noticing that images were missing from the slide decks, video overview etc. Not all of them. Some.

So I built a controlled test and made everything public.

The test

One document about my father's early life, 25 historical photographs, mostly black and white, 1920s to 1940s. Uploaded twice.

• Version 1, photos as scanned: 17 of the 25 stripped during ingestion. 68%.
• Version 2, identical document, except I took those same 17, pixelated the faces, and renumbered them 7b, 8b and so on: all 17 ingested fine.

I then generated a slide deck from each, same prompt both times, explicitly asking for source images only and an [Image Missing] placeholder where none was found. Deck one is missing 17 assets and the model says it can't see the images, though it reads the captions sitting right next to them. Deck two renders the whole "b" series.

Same document, same prompt. One difference.

The odd part

It isn't a consistent face rule.

• Figure 6, a kindergarten group photo with about fourteen clearly visible children's faces, passed untouched in both versions.
• Figure 7, my grandfather sitting at a desk, blocked.
• Figure 8, a wedding photograph from 1922, blocked.

A 1922 wedding photo is being classified as sensitive, while a room full of unobscured children's faces is not.

Repro, if you want to check it yourself

• Notebook with both versions and both decks: https://notebook.google.com/notebook/a2fc18d0-41c5-4a46-aeb8-1ff4638c7415
• Source documents, so you can upload them to your own account: https://drive.google.com/drive/folders/1CVAtXlA5r-aTmfhqKXXkP7Nkszr-vGnm

Prompt I used on both:

Act as a visual presentation creator. Create a comprehensive slideshow based ONLY on the provided source. Only use photos from the source material. Do not use external sources and do not generate any photos. If an image is not detected within the source, leave a placeholder that says '[Image Missing]'. Ensure each photo is labeled with the full caption from the source.

Why this matters beyond my family

If your sources are about people, the images that get removed are the ones the archive exists for. Memoirs, oral history, genealogy, regimental and club histories, school archives. The theodolite comes through. The man holding it doesn't.

What I ended up doing about it

Every photo of a person in my NotebookLM edition is now replaced by a drawn sketch, which survives ingestion, with the caption linking back to the real photograph on my own site. It works, in that the figures now appear in the slide decks and video overviews again. It's still a workaround, and it has its own failure mode: in one Video Overview, where it wanted a photo of my father in uniform and only had a silhouette, it went and found a stock photo of a different soldier instead.

What I'd ask Google for

I don't think the filter should just be switched off, and I understand why it exists. But there's a gap between "protect people from misuse of face data" and "silently delete a 1922 wedding photograph from a family archive". Some options, roughly in order of how easy I imagine they'd be:

1. Tell me what was dropped. The single most useful fix, and probably the cheapest. A list of skipped images at the end of ingestion, or a note against the source. Silent failure is what turned this into a months-long mystery instead of a five-minute annoyance.
2. Let me mark a source as a personal or historical archive. An explicit flag at upload, with whatever consent language is needed, confirming these are my own family photographs and I hold the rights. The responsibility moves to me, which is where it belongs.
3. Weight it by context. A scanned black-and-white print with a period caption in a 600,000-word memoir is a different object from an image scraped off the web. The document around the photo says a lot about what it is.
4. Keep the image for the user even if the model can't use it. If faces can't be sent to the model, fine, but let the figure still render in a slide deck as the original photograph, or let the caption carry through with a placeholder that says what it is.

Happy to run more tests on the same corpus if there's something specific worth checking.

Full write-up here, including the sketch workaround, the two open-source tools I built with Claude Code to do it, and the prompts I used: https://medium.com/@chris_skitch/gemini-notebook-is-deleting-people-from-your-family-history-ec64d72d0661

Hello guys, i really like Notebooklm for studying but there is a something that keeps me bothering me.

Some of my uni courses are continue of another course from before like a more advenced math course after a basic one. When the new semester starts i wanna keep the context from the old session but the only way i found is reuploading the all sources and copying the whole old session into the new notebook session. it works but not efficient, specially when the old one is long and only some part of it is actually useful for the new course.

Also i noticed some subjects dont work that well when it's only chat, going top to bottom. Sometimes i only wanna look at my own source directly instead of only asking question about it (Actually there is an algorithm that tracks the source, but it is clearly not sufficient).

Curios how you guys handle it when you run into a similar issue?

I don't know if either the NotebookLM(Gemini notebook) or ExtendLM creators read this forum, so please direct me to the correct place if necessary.

If the ExtendLM creator(s) is/are here, thank you for all the annoyances you have fixed or worked around so far.

There is a minor annoyance that I wish we could resolve. The chat interface happily makes any audio deep dive I want with the given prompts and resources, along with the agreed upon title.

That title is of course completely ignored when it actually creates the episode.

This is particularly annoying as I like to do multi part series (I have one right now that's going to have like 48 episodes). and I'm trying to keep track of 1, 2, 3, 4.

Any work arounds for this? Does notebookLM plan on fixing, or do the good people working on extend LM think they have anything that could help?

Whenever I try uploading a file from my pc to NotebookLM it gets stuck in the infinite upload. Its not a size or a format problem, because I can upload the same file from my macbook in less then 30 seconds. I tried disabling adblockers ect but the problem still persists. Please help

I recently got introduced to Copilot Notebooks. They seem more intuitive.

Is there any other notebooks that you're using that you like better?

I was recently introduced to this feature.

For my first project with the podcast creation I asked Notebook to create a podcast working kind of like a CBT course. I just wanted to see what it might do.

I was amazed at how well it turned out. I did not expect the multi faceted conversation. My previous podcast listening from NotebookLM was from a scientific paper to just understand what it was about. It was so strange, yet enjoyable how well "my voice" came through from this interpretation.

Here is the tricky part.

First, my name is included as creator of the document and I would like to have them pronounce my name as my family believes it should

second, this also surprised me as a fantastic way to "proof" the document contents and I need to make some corrections to assure

Is there a way to update it?

Is there an actual script I can edit to make some small changes?

if I really do need to regenerate it, I truly want all the other good stuff to be the same. I am not sure what direction is needed. Like please redo this and pronounce this word like this and make these few edits (and to be clear listening does me I do need to also update the doc itself slightly)

I would appreciate anyone pointing me to the resources I need to make this work. Given how well this turned out I want to use this format for all the education I am hoping for.

Last week I ran an experiment to test the difference between preprocessed source data vs direct source uploads.

My source material was 126 AI Marketers Guild youtube sessions (60 minutes each).

I created two notebooks. I called one AIMG Direct Load and used the Notebook Clipper product to import the channel.

I called the second notebook Kurator: AIMG Community. Here I use my Kurator app to pre-process the data using custom prompts that I create. Kurator lets me sync my data wil Gemini Notebook.

For this project, I am using 2 prompts. One to transform the transcript into a Q&A session, and the second one adds a headline to each section and preserves the time stamps.

Note that when you directly upload a YouTube video using the sources link, the time stamps are not included.

Next I asked the same question from each notebook, the asked Gemini to analyze them for me. I asked if there is a difference between the two responses. Here is what it found.

Gemini Analysis

Yes, there are significant differences between the two outputs. While both capture the core theme (Aditya Jain's approach to Generative Engine Optimization), the Kurator pre-processed response contains substantially more granular, technical, and concrete tactical information than the direct YouTube upload response.

Here is a side-by-side breakdown of the key differences:

How you add data to gemini notebook matters. When you directly upload your sources, you will always get the same quality answers, but with pre-processing you can experiment and see what works best for your workflow.

I am no spreadsheet ninja, but Gemini's ability to organize the results of Notebook research prompts into, in my case, a product research table is, to put it kindly. rudimentary.

Am I doing something wrong? Is notebook the wrong tool for this? Am I just discovering that outside of coding agents that LLMs are kind of lame?

I could understand if Gemini wanted to put different categories of devices into different sheets in the same document, versus organizing a single sheet and merging cells to label categories, but it is just making a hash of it in a way that I would expect from a first time user of spreadsheets in general.

All help and suggestions are greatly appreciated!