So I spent, I think, an entire evening just recently
trying to cross -reference these three different
internal reports, just looking for one specific
number. We're all just pile up information, aren't
we? PDFs, articles we save, transcripts from
videos. Sometimes it feels less like a library
and more like, I don't know, just drowning in
data. Oh, yeah, it's the classic research problem,
right? But it's amplified now with all these
tools. We need info fast. But then you turn to
a general AI, and sometimes it just starts. Well,
making things up because it's looking at the
whole messy internet. Right. So imagine if you
had this AI assistant, but it was totally personalized,
a research guide that only and strictly uses
your trusted information. It basically stops
that whole hallucination problem because, well,
it can't talk about something it hasn't read
from you. And that's exactly what we're digging
into today. We're exploring a notebook LM. It's
this really interesting tool from Google. It
fundamentally changes how you can organize and,
more importantly, use all that knowledge you've
gathered. OK, so today we'll break down the core
tech behind it. We'll look at the workspace,
which is actually pretty simple. Then we'll walk
you through setting up your first notebook focused
on your stuff. And then we'll get into the cool
creative tools like making quizzes automatically
or even presentation videos. This is really about
giving you a way to master large chunks of information,
but specifically targeted to the data you've
already collected. Okay, let's get into it. So
fundamentally, Notebook LM is built to be your
personal research assistant, super focused. But
what's the single biggest tech difference between
this and just a standard chatbot? It really boils
down to the constraint. It's forced limitation.
Standard AI looks everywhere, the whole web.
Notebook LM is strictly limited. Only the documents
you upload, your PDFs, your notes, your transcripts,
nothing else. And that's what makes it genuinely
personal and maybe most importantly, trustworthy.
So if I upload, say, 15 really specific files
about internal project metrics, the AI can only
quote from those 15 files. It won't bring in
anything else. Exactly. That's it. And the tech
that makes this happen, that guarantees it, is
called RAG -G, Retrieval Augmented Generation.
Basically, RAG just means in simple terms. The
AI first reads and kind of indexes your documents.
Then it uses only that reading to generate answers.
So the answers are accurate. You can check them.
It's like a closed loop. Huh. So it's almost
like a lawyer arguing a case. They can only use
the specific evidence you handed them. They can't
just speculate or pull something from across
the street. That's a great analogy, yeah. Perfect.
The output is always anchored in the evidence
you provide. OK, so then if the AI is strictly
limited like that, only using my sources, how
does that really change the quality, the reliability
of the answers I get? Well, the answers become
really accurate. You can trust them. And crucially,
they're always tied directly back to your original
source material. So when you first open it up,
the interface, it's really built for function.
It avoids confusion. It's basically split into
three main parts, which makes handling complex
research, well, simpler. OK, so on the left,
you've got your library. That's like your personal
bookshelf, right? For all sorts of files, Google
Docs, PDFs. website links, even YouTube transcripts,
that's where your raw stuff lives. Then right
in the middle, that's the conversation space,
that's your main workspace, that's where you
actually chat with the AI, ask it to summarize
that long technical manual, or maybe compare
ideas from two different files you uploaded.
And the really key thing there, the quality check,
is the citation. Every single thing it says every
fact has this little number next to it, right?
And it links directly back to the exact spot
in your original document So you can check it
instantly super important. Yep, and then over
on the right. There's the creative toolbox They
call it the studio panel. Think of that as the
workshop That's where the AI takes your simple
data and turns it into useful things quizzes
mind maps flashcards that kind of stuff The flexibility
seems like the big win here. Students could use
it for prepping for complex exams, easily grabbing
citations for essays. Writers could use it to
organize tons of research from different articles
for a book or song. Absolutely. And for professionals.
Imagine you get that 50 -page business report
Monday morning. Dense stuff. Instead of spending
hours reading it, you just feed it into the conversation
space and ask, okay, what are the three main
risks here and how do they suggest mitigating
them? Boom, you're ready for the meeting. Right.
So beyond just studying your reading summaries,
what's maybe the fastest way a professional could
use that citation feature? especially when they're
on a tight deadline. Those quick citation checks
just build credibility instantly. You know, I'll
admit, I still wrestle with prompt drift myself
sometimes. So being able to quickly verify the
AI's claim against the source, that's essential.
Okay, let's make this practical. Let's walk through
setting up a notebook. Say we're researching.
Sustainable living. Pretty common topic. Sure.
So step one is easy. Sign in, click PUP plus
new notebook. Step two is all about the sourcing.
This is where you feed the AI your knowledge
base. You can pull files from Google Drive, upload
that important PDF guide you found, paste in
a Wikipedia link. And this is cool. Paste a YouTube
video link. It'll grab the whole transcript.
Okay, so everything's loaded in. Now you start
the conversation, right, in that chat window.
But the key is getting specific with your prompts,
isn't it? Not just general questions. Absolutely.
You've got to move beyond what is sustainable
living. Ask for something specific like, are
there any tips about saving water mentioned in
the PDF and the transcript? Or maybe a comparison
prompt. Compare how the Wikipedia article defines
sustainable living versus how the YouTube video
explains it. Get specific. And again, every answer
it gives is grounded by those little clickable
citation numbers. It's like built -in fact checking.
Makes the whole thing reliable. Yeah, it's like
having a super efficient librarian who points
you to the exact sentence on the right page every
single time. Cuts down massively on that time
you'd normally spend checking footnotes. That
sounds really useful. But what about, what if
I feed it two sources that totally clash? Like,
one article says plastic recycling is great and
another says it's basically useless. Does the
AI try to pick a side or smooth it over? Good
question. No, it's designed to actually highlight
the conflict. It'll usually say something like,
source one says X, but source two says Y. It
presents the differing views with the citations
so you can see the conflict and decide based
on the evidence. It won't create some kind of
false consensus. Okay, that makes sense. So,
say I've just started looking into this topic.
How can I find more trustworthy info without
having to leave the notebook interface? Ah, yeah.
You can use the new Discover Sources feature.
It suggests related, high -quality sources, often
from places like universities or research groups
based on what you've already uploaded. All right.
So we've talked about pulling information out.
Now let's shift to creating things with it. That's
where the Studio Panel, the creative toolkit,
comes in. This seems like where the AI goes beyond
just summarizing. It actually starts generating
useful, finished, knowledge products for you.
Yeah, and the strategic value here is important.
It's not just about, say, making flashcards.
It's about making flashcards based only on the
specific technical terms from your proprietary
documents that you need to memorize. It's targeted.
OK, like that discover sources feature we just
mentioned. Yeah. That sounds like a real research
booster. It looks at your stuff and then actively
suggests related academic sources. And you can
get really specific with the prompt there, too.
Oh, yeah, definitely. You could ask something
like, Based on my documents about recycling and
composting, find me five recent academic papers
on the circular economy. And also effective waste
management policies, specifically in major port
cities in Southeast Asia. I mean, that's a pretty
sophisticated targeted research request in just
one go. Then you got the study tools, like quizzes.
It just makes them automatically to test your
knowledge. Exactly. And you can demand really
specific kinds of quizzes, too. You might tell
it, create a quiz, make it 15 difficult questions,
mix of multiple choice and true -false, and focus
only on the stats and scientific terms from these
specific science reports I uploaded. Ignore the
general concepts. It tests exactly what you need
it to test. And flashcards for just straightforward
memorization. Like, make me 30 flashcards to
memorize key definitions about sustainable farming
techniques. on things like permaculture or hydroponics
from my sources. Yeah. And personally, I really
like the mind maps. For visual learners, seeing
that big picture is so helpful. It lays it out
visually. You know, like sustainable living in
the middle, then main branches like reduce waste,
save energy, and then smaller branches off those,
like composting, recycling. It turns a dense
topic into something you can see. These tools
really do seem to move way beyond just summarizing.
So how quickly can they actually take us from
just raw data, I mean, messy notes, to a finished
maybe even professional -level output. Whoa.
I mean, imagine creating a whole structured presentation
video, fully sourced from Documency Trust, in
just minutes. It drastically speeds up both the
learning part and the actual production part.
And that speed in production brings us to the
really polished, media -ready outputs. First
up, there's the audio overview. This thing turns
your documents into a short, kind of flexible
audio summary, like a custom mini podcast just
for you, based on your stuff. That sounds great
for absorbing complex info while you're, like,
commuting or exercising. You could prompt it
something like, make a seven -minute audio overview,
use the style of a thoughtful documentary narrator,
tell a story about plastic pollution's impact,
and present the solutions found in my notes.
Pretty cool and then the video overview. Honestly,
this one feels pretty groundbreaking. It automatically
generates a three to four minute presentation
video We're talking dynamic slides a voiceover
all created automatically and based only on your
source documents Wow, so you could build a professional
looking deck on say the impact of fast fashion
You'd tell it to structure the video into maybe
environmental costs, social impacts, and sustainable
alternatives, feed it the source articles and
reports, and you've got a presentation ready
in minutes. Pretty much. And it's not just for
English speakers. You can generate the script
and slides in other languages too, like Vietnamese,
for example. Makes your research accessible much
more broadly. That speed is definitely incredible.
But it does make me wonder, if you generate a
video presentation, in, say, three minutes, instead
of spending 20 hours crafting it, does that maybe
bypass some of the critical thinking needed to
really internalize the material? That's a really
thoughtful point. I think the value shifts, you
know? You save a ton of time on the design aspect,
but you probably need to spend more focused time
on the prompting, making sure the structure is
exactly right. and then critically verifying
the content using those citations. So the critical
thought moves maybe from creation to validation
and synthesis. It's a different kind of engagement.
OK, so to make sure that synthesis is high quality,
we have those three best practices. First one,
quality over quantity. Garbage in, garbage out.
Always holds true, right? Absolutely. Second,
be specific. Don't just ask vaguely, Tell me
about recycling. Ask. List the step -by -step
process for recycling plastic bottles correctly,
based only on the guidelines in Document X. Specific
prompts get accurate results. Makes sense. And
third, break down big topics. If you're tackling
something huge like, I don't know, the entire
history of the space shuttle program, maybe split
it up. Make smaller, focused notebooks, one for
design, one for missions, one for the end of
the program. Keep the AI focused on manageable
chunks. Right. So given how fast these AI tools
are changing all the time, what should listeners
maybe keep an eye out for? What should they prioritize
checking for next week with Notebook LM? Yeah,
good question. I'd say just check back often.
See what new creative tools pop up or what new
kinds of sources you can upload. The pace of
updates is pretty fast. And it's all aimed at
helping you learn faster and get more inventive
with your own knowledge. So when you step back,
Notebook LM really feels like it changes how
we interact with our own knowledge. It's not
just another general search tool. It becomes
this personalized, creative, and importantly,
highly accurate assistant for managing knowledge.
Yeah, the goal shifts, doesn't it? It's not just
about collecting facts anymore. It's about actively
using them, synthesizing them, creating new things
based on them. Outputs you can actually trust
because they're tied directly back to your sources.
So the question for you listening is, what complex
50 page report or maybe what huge online course
or lecture series are you going to finally master
using these kinds of features? It's time to stop
feeling buried by all that data and actually
start organizing that knowledge effectively.
Yeah, just start building that first notebook.
You might genuinely be surprised how fast you
can take maybe years of saved articles and notes
and turn them into a clear, verifiable summary.
We'll catch you on the next Deep Dive.