Google Notebook LM for Knowledge Management
Key Points
- The main challenge many face is feeding large amounts of information into an LLM while keeping the output consistent and trustworthy.
- A personal Retrieval‑Augmented Generation (RAG) system is the ideal solution, but most non‑coders lack accessible tools to build one.
- Google’s free Notebook LM provides the most accurate, low‑hallucination search and summarization across extensive documents, even handling 60‑plus‑page files with precise citations.
- Notebook LM also offers versatile output formats—audio overviews, video, mind maps, reports, flashcards, and quizzes—to help users learn and extract “gold” from academic papers, transcripts, or project notes.
- Because of its reliability and learning‑focused features, Notebook LM is an under‑utilized resource for anyone needing organized, searchable knowledge without coding expertise.
Sections
- Using Google Notebook LM for Knowledge Management - The speaker recommends Google’s free Notebook LM as a non‑coding‑friendly retrieval‑augmented generation tool that reliably stores, searches, and summarizes large document collections with minimal hallucination.
- Notebook LM: Strengths, Limits, and Use Cases - The speaker explains Notebook LM’s accurate, multi‑source capabilities, its ideal role in theme‑based project organization and selective retrieval, and its drawbacks such as limited writing ability and memory challenges.
- Free Drag-and-Drop RAG Tool - The speaker lauds Notebook LM as the best, no‑code, free retrieval‑augmented generation system despite its lack of chat saving, and promises a forthcoming guide on use cases and prompting strategies.
Full Transcript
# Google Notebook LM for Knowledge Management **Source:** [https://www.youtube.com/watch?v=XOEMnCOTvnI](https://www.youtube.com/watch?v=XOEMnCOTvnI) **Duration:** 00:08:54 ## Summary - The main challenge many face is feeding large amounts of information into an LLM while keeping the output consistent and trustworthy. - A personal Retrieval‑Augmented Generation (RAG) system is the ideal solution, but most non‑coders lack accessible tools to build one. - Google’s free Notebook LM provides the most accurate, low‑hallucination search and summarization across extensive documents, even handling 60‑plus‑page files with precise citations. - Notebook LM also offers versatile output formats—audio overviews, video, mind maps, reports, flashcards, and quizzes—to help users learn and extract “gold” from academic papers, transcripts, or project notes. - Because of its reliability and learning‑focused features, Notebook LM is an under‑utilized resource for anyone needing organized, searchable knowledge without coding expertise. ## Sections - [00:00:00](https://www.youtube.com/watch?v=XOEMnCOTvnI&t=0s) **Using Google Notebook LM for Knowledge Management** - The speaker recommends Google’s free Notebook LM as a non‑coding‑friendly retrieval‑augmented generation tool that reliably stores, searches, and summarizes large document collections with minimal hallucination. - [00:03:46](https://www.youtube.com/watch?v=XOEMnCOTvnI&t=226s) **Notebook LM: Strengths, Limits, and Use Cases** - The speaker explains Notebook LM’s accurate, multi‑source capabilities, its ideal role in theme‑based project organization and selective retrieval, and its drawbacks such as limited writing ability and memory challenges. - [00:08:21](https://www.youtube.com/watch?v=XOEMnCOTvnI&t=501s) **Free Drag-and-Drop RAG Tool** - The speaker lauds Notebook LM as the best, no‑code, free retrieval‑augmented generation system despite its lack of chat saving, and promises a forthcoming guide on use cases and prompting strategies. ## Full Transcript
What do you do when you want to give
your LLM, your AI, more than will fit in
a chat? And how do you keep it
consistent information that you can
trust? If you have a lot of information
and you have to like squeeze it into an
AI, I get this question a lot. People
are like, "How do I organize all my
notes? How do I learn a new topic? Nate,
you have so many articles. What do I do
with them?" The answer is a personal
retrieval augmented generation system,
but for people who are non-coders,
that's not super accessible. I have been
looking and looking and looking for the
past few months for the best answer for
this. I got to tell you the best answer
is free and it's Google. Google has
launched Notebook LM and nothing comes
close to beating it for the ability to
learn complicated subjects, the ability
to have lots of documents in one place,
the ability to have reliable search
across those documents. I got to be
honest with you, the LLM search with the
lowest hallucination rates right now is
Notebook LM. There is nothing else that
comes close. It is extremely precise
about what it recalls. But let me show
you. I always think examples are more
helpful here. This is an actual document
from one of my articles a few months
back where I talked about Microsoft
Copilot. The document is 62 pages long.
It is a long read. What if you don't
have all the time in the world and you
want to just understand what's relevant
for you? Well, Notebook LM is honestly
the best tool out there for figuring
that out. And you'll note that in this
case, even though I could add lots of
other sources, I've chosen just to have
a conversation with this doc so that I
can get exactly what I want. I could add
more if I wanted. I'm not going to. It
gives me a summary of what's in the doc.
This is very accurate. I wrote it. I
should know. Uh it gives me specific use
cases for non-coders because I asked for
that. And so it goes through and gives
me a really great answer based strictly
on the document. It cites everything
it's getting. So you can actually see
where it's citing it, which is
phenomenal. Uh, and then it comes down
and I can ask, well, give me some
non-obvious use cases, right? And it's
going to give me like specific use cases
also in the doc. This is a way to get
gold out of these longer documents. Not
just for me, but like for any situation
where you have say a longer academic
paper or a complicated subject to learn
about or let's say you have transcripts
and notes for a particular project or
maybe with a client and you just need to
get them organized somewhere and talk
about them and get very accurate
retrieval. You can do that. And by the
way, if you're not a reader, look at how
you can work with this in non ready
ways. Audio overviews work really well.
There's a video overview. There's a
mindm map option, reports, flashcards,
quizzes. This is designed to help you
learn. We are sleeping on Notebook LM
right now. And that's why I want to talk
about it. So, what is it that makes me
confident that I can recommend this to
people who just need a personal rag
system, a personal system for notes,
etc. Fundamentally, it is the
combination of accuracy and a project
mindset. Everything in Notebook LM is a
project. And so you can put any project
orientation you want. You could have a
project per client, a project per
subject. You can do both. And all you
have to do is put the links you care
about, put the articles you care about
into that project. And it's so easy to
add more docs. It will accept just about
any file type uploaded. And it will also
make it very easy for you to link any
file you want on the internet. So if you
are behind a payw wall, you can just
download an academic journal of some
sort, upload it to notebook LM and it
works great. That is why I think it's
the most flexible tool for that use
case. Now, could I have recommended to
you a custom coded implementation using
Obsidian and a local language model?
Yes, I know people who do that who are
engineers. It is super helpful if you
are an engineer and you're willing to
build your own retrieval augmented
generation system using a local AI. Most
of the people I talk to who desperately
want something that they can use at home
to organize stuff and talk with AI that
is much bigger than a chat window. They
are not going to code. And so I wanted
to find something that's useful. And I
want to be honest with you about where
it works as a user and where it doesn't.
Where are the weaknesses? Because to be
honest with you, this gets back to the
memory problem. There is no perfect
solution. This is the best solution we
have at this time, but there is no
perfect solution out there right now.
What is good about Notebook LM is, as
I've been mentioning, the accuracy,
which I think is a super important
feature, the ability to upload lots of
sources, and so it gets uh you can do
dozens and dozens of sources in one
project. The ability to add lots and
lots of projects, the multimedia
outputs, the audio, the visual, I showed
you those. Those are all great. The
drawbacks are not killer features, but
they're kind of unfortunate. I find
personally that the most effective use
for Notebook LM is actually to organize
projects by theme and then to take
selective searches like I showed you
with Copilot there and output the
selective searches like slices of
accurate context into a thinking LLM to
finish up work because Notebook LM is
not a great writer. Notebook LM is not
super heavy on thinking. It tends to be
focused on retrieval. In fact, those two
people don't know this, are kind of
opposites. If your LLM thinks more, it
is more likely to draw from its
parametric weights. It's its own
internal LLM training data to answer
your questions instead of looking at
retrieving accurate information and
putting it in front of you. And Google
has correctly chosen that for this
application, you want to retrieve
accurately. And so you get a very
accurate slice of data back, but not a
lot of thinking with the data. I look at
Notebook LM as a chance to extremely
accurately summarize and extract slices
of information I care about and then I I
use copy and paste and I pull that out
and I put it into an LLM when I need to
think about it more. That is not a
workflow that I see very often, but I
think it's super important. You need to
be able to understand that AI doesn't
all do the same stuff, right? We talked
about perplexity and how perplexity is
AI native yesterday. Well, in the same
way, this is a retrieval native system.
It is focused on retrieving from a rag,
but it's very tightly constrained, even
more tightly constrained than
perplexity. So, it's super accurate. And
that means it's not super cognitive,
right? It doesn't think a ton. And
that's the drawback that you get. I
think if you're trying to build an
evergreen note system for all of your
notes and your notes go back for years,
it will be difficult to pull them into a
system like this. That is a situation
where I think investing in a customuilt
solution is going to be more meaningful
for you because if you have, you know,
tens of thousands of notes, it just
isn't at that scale. Where Notebook LM
really shines is at a smaller scale
where you have dozens to maybe a hundred
or two sources and you want to look at
these related sources in a very coherent
way and retrieve things. In my
experience, that is actually most
people. For example, if you're trying to
understand what your client has done in
the last 6 months, you can upload recent
client emails in documents or link them
from Google really easily. You don't
necessarily need 20 years of client
history because you have that in your
head if you're a longtime business owner
and you can bring that to bear and you
get 80% of the value with much less
effort than custom coding. Similarly, if
you're trying to build a knowledge
system about AI, you can have the
flexibility to add a particular project
for a given AI topic, learn about that,
have all of that knowledge in a project
and then move on to the next thing when
AI evolves and you stay very focused on
the cutting edge, but have all the old
project files there. The one drawback
that I think is most painful about
Notebook LM that I want to be honest
about is right now it doesn't save your
chats. And so when you're chatting it
feels really fluid, but you had better
copy and paste out what you care about
because otherwise you're going to have
to recreate that chat the next time you
go in. I don't know why Google did this.
I think it's silly, but there we are. It
doesn't save your chats. Despite all of
that, it is easily the best rag system
out there all things considered. It is
the least technical. It is drag and
drop. No code required and it's free,
which is not trivial when everybody else
is asking you to pay stuff. And so, I
think it's super relevant. I hope that
you've enjoyed this quick tour through
Notebook LM. I'm going to put together a
guide of use cases and also some
suggested prompting tips because again,
when you're doing retrieval prompting,
it's very different from doing cognitive
prompting. So, I'm going to get into
that in the substack as well.