You know that feeling, right? Like you're just
drowning in information. Oh, yeah. Articles,
reports, videos, social media. It's just stuff
everywhere. It is. It's overwhelming. And trying
to feel genuinely well -informed like you've
really got it without spending hours and hours
sifting through it all. Man, it's tough. It really
is. And that overwhelm can actually stop you
from even starting sometimes. Totally. You see
the pile and you just think, where do I even
begin? Exactly. But what if, what if there were,
you know, new tools coming out that could actually
help cut through all that noise? Tools to help
manage it. Yeah. Get you to the valuable insights
after specifically from your stack of sources.
It's kind of what we're diving into today. Okay.
We've got this source that talks about two specific
AI research tools, Perflexity and Notebook LM,
and how like you can actually use them together.
Ah, so a combo approach. Right. To process information
you bring to the table. What's interesting there
is the idea of not just one tool, but a workflow,
you know, leveraging their different strengths
to get deep into your material. Yeah. So the
mission for this deep dive is really to unpack
this source, right? To do it. To show you how,
according to this article. These specific tools
can help you take your sources, whether you're
prepping for a meeting, learning something totally
new, or doing research for really anything. Right,
any kind of knowledge work. Gather more around
it, analyze your core material, and ultimately
boost whatever project you're working on by finding
those key insights within that material. It sounds
like it's about efficiency, but also focus. Getting
insights from the documents you actually care
about. Exactly. Without getting lost in the wider
web or having the AI, well, make stuff up. Right.
The hallucination problem. And the core concept
this source teases is pretty simple, but I think
powerful. One tool for casting a wide net, helping
you gather sources. Yeah, as a discovery phase.
And the other for really focused analysis using
only the material you brought in. Which directly
tackles that major issue with large language
models, the, you know, hallucinations. Definitely.
When they just invent answers based on their
massive training data instead of sticking to
what's actually in front of them. Ah, the dreaded
hallucinations. Okay, so the idea here, according
to the source, is you use a tool like Perplexity
for that initial broad sweep. Uh -huh. Finding
reports, articles, maybe even discussions on
forums, videos. It's like super fast at discovering
potentially relevant sources online. Right. But
then when you need to analyze your specific documents
reliably. That's where the second tool comes
in. Notebook LM. Yes. Notebook LM is designed,
supposedly, to answer only from the sources you
import. It builds a sort of knowledge base just
from your files or links. Okay, so that's the
key difference then. Perplexity helps you find
stuff out there, maybe even organize potential
sources. Yeah, the gathering part. But when you
need to analyze your stack, the reports, the
articles, the notes you've collected reliably,
Notebook LM is meant to be the laser -focused
one. Right. Supposedly minimizing those hallucinations
because it's strictly grounded in your stuff.
It doesn't look outside that. Okay, that makes
sense. Exactly. So when you combine them, you
get the best of both worlds potentially. Broad
data collection from perplexity to help build
your source list. Uh -huh. And then that focused...
hopefully accurate analysis from Notebook LM
grounded purely in those specific sources you
selected. Right. And the source says this makes
them suitable for all sorts of projects, academic,
business, creative. Seems pretty versatile, you
know? Yeah, I can see that. Use the wide net
first. Find the good stuff that's out there.
Then bring the important pieces into the, like,
secure analysis zone. The walled garden of your
own information. Yeah, that only talks about
your material. Uh -huh. The source actually goes
through some specific examples of how this combined
approach can help you pull insights from different
types of source stacks you might have. Okay,
sounds good. Let's unpack a few. What's the first
one? Market trend analysis. Let's say you've
been tasked with understanding what's happening
in a specific industry. Okay. Maybe you've collected
a bunch of recent reports and articles using
something like perplexity. Right, like researching.
responsible AI trends. I've got these PDFs from
consulting firms, maybe some research papers
I found. Perfect. So you used a tool, maybe perplexity,
to find those high quality sources searching
for reports from Deloitte McKinsey published
in the last year, let's say. Got it. Gathered
those links, downloaded the PDFs, that initial
search and collection phase. Exactly. Then you
bring those specific reports and papers into
Notebook LM. Okay. Upload them. Right. Now, instead
of reading all 200 pages across three different
reports, you can ask it questions based only
on those documents. And it's supposed to tell
you what your sources say and not just a general
web answer. Precisely. Based on the stack of
reports you provided on responsible AI, Notebook
LM could tell you, for example, that your sources
consistently highlight increased adoption and
the growing importance of human in the loop oversight
as major shifts. And crucially, it should point
to the specific sections in your documents where
it found that info. Oh, well, so I wouldn't just
get a generic AI answer. I'd get an answer from
my specific reports like Deloitte mentions increased
adoption on page 15, driven by regulatory pressures,
while McKinsey discusses human in the loop on
page 32, emphasizing ethical considerations.
Something like that. Yeah, exactly. It's grounding
the insights strictly in what your documents
actually say. OK. You can also ask it based on
those reports. What's driving companies to invest
in this topic? Right. And it would pull out motivations
mentioned in your sources like regulatory pressures
or maybe market demand. If those drivers are
present in the reports, you fed it. And if my
sources don't mention market demand? Notebook
LM shouldn't invent it. It should basically say
that's not mentioned in the provided documents.
That's the key difference, right? It won't guess.
It just says, nope, not in this stack. Okay.
Market trend analysis using curated sources.
That seems powerful for getting specific answers
from your research. What about another example?
How about product enhancement research? Imagine
you're a product team. You've collected a bunch
of user reviews for competing products, maybe
related ones too. Yeah, scrape from Keptera or
G2, read some threads from Reddit, watch some
YouTube reviews, that kind of thing. Exactly.
You've got this pile of user feedback and competitor
reviews for, say, email marketing tools. You
want to enhance yours. Okay, got it. I've got
my pile. Great. So you've used tools. maybe Perplexity's
focus search on review sites or Reddit. To gather
those specific reviews and feedback, you got
the text, the links, whatever you collected.
Found the good stuff. Check. Then you bring all
that user feedback data into Notebook LM. Right.
Now you can start asking it to synthesize insights
across all those individual reviews and pieces
of feedback you imported. Like what are the common
pain points customers mentioned across all these
reviews? Exactly. Based only on the stack of
reviews you imported, Notebook LM could tell
you that across the board, users seem frustrated
with technical glitches during setup. Or maybe
limitations in the email design templates or
slow customer support. It's summarizing themes
present in your specific source material. Or
I could ask, what specific features do users
consistently mention as missing or desired to
identify opportunities based on that feedback
stack? Totally. And here's where it gets really
interesting, according to the source. OK. You
can upload your own product's website page, maybe
the main features page, into Notebook LM alongside
all that competitor review data you gathered.
Oh, wow. So my stuff and their stuff. And then
what? And then you can ask Notebook LM, grounded
in your product page. Whoa. That's pretty cool.
Getting AI to give you concrete suggestions based
on your stuff and the research you collected.
It's not just summarizing. It's helping with
the analysis and... Like application. It really
streamlines that phase, yeah. Another example
they give is audience research. Ah, understanding
your target audience. Okay, say I've got a stack
of surveys, reports, articles about parents'
needs for child care services. Perfect example.
So you use something to find those recent surveys
about working parents' needs, maybe research
reports using specific site operators and perplexity
to focus on academic or organizational sources.
Right. Found the relevant PDFs and links. You've
collected them. Built up that knowledge base
about the audience. Got it. Right. Now you bring
all those surveys, reports, and studies into
Notebook LM. Okay. And you can ask it to synthesize
what your specific sources say about this audience.
Like, what are the biggest challenges parents
mention about child care? According to these
surveys I uploaded. Precisely. Based only on
the documents you imported, Notebook LM could
tell you that your sources highlight availability
issues, maybe concerns about quality, or a perceived
lack of learning programs as major challenges.
It extracts these key points directly from your
research. Or I could ask. What are the primary
considerations when parents choose a child care
service, according to these reports? To pull
out factors like cost, location, convenience,
maybe branding. Again, only if my sources actually
mention them. Exactly. And just like the product
research example, you can upload your own child
care business website into Notebook LM alongside
all that audience research. OK, so bring in my
own site again. With the research this time.
Yep. And then Ask Notebook LM, grounded in your
site content and all that audience research you
gathered, suggests the top three changes to our
homepage messaging based on this research. Okay.
It might suggest things like prominently featuring
information about flexible hours. If the research
you provided indicates that's a significant need
parents mention, but your current site doesn't
really highlight it. Wow. And I could even ask
maybe what needs highlighted in their research
are not currently mentioned on our website. could
emphasize. To find things like, I don't know,
learning philosophy or staff qualifications.
If my research says parents care about that,
but my site isn't featuring it prominently, that's
wild. It is. It's like having an AI consultant
analyze your website against market research
you provided. It really is. Audience research
seems like a really strong use case for this
kind of focused analysis on your own material.
Yeah, definitely. What about content creation?
Like planning a podcast, maybe? Yeah. OK, say
I've got. Reviews of successful leadership podcasts,
maybe some transcripts of popular episodes. Perfect
example. So you've used tools to find those top
leadership podcasts, maybe gathered reviews from
sites like Listen Notes, perhaps even downloaded
some episode transcripts or show descriptions.
Right. Got my raw material on the competitive
landscape and what's already out there. Exactly.
Now, you bring all those reviews, show descriptions,
transcripts, whatever source material you've
gathered into Notebook LM. Okay. And you ask
it to analyze that specific content. So I could
ask it, how do these specific podcast shows describe
their ideal listeners? Based on their show descriptions
and reviews that I put in. Precisely. Grounded
only in the material you imported, it could synthesize
descriptions of target audiences, specific job
roles, experience levels, company types mentioned
across the shows you provided. It's analyzing
my competitive intelligence file, essentially.
And I can ask it to compare the positioning of
these shows. What unique angles do they emphasize?
Based on my input. Totally. Based on your stack
of show descriptions and reviews, Notebook LM
could identify how different shows are carving
out their niche. Which helps me figure out how
to position my leadership podcast to stand out.
Exactly. To stand out in that specific competitive
set you researched, it seems super practical
for content creators analyzing their space. That
is super practical. Okay, makes sense. Finally,
there was a fifth use case mentioned. Yeah, learning
new subjects. Let's say you're diving into something
totally new, like... Mm -hmm. Marketing psychology.
Oh, this is big for a lot of us. Just learning
something complex from scratch. Okay, I've collected
a bunch of articles, maybe some lecture transcripts,
a few relevant podcast episodes on marketing
psychology. Great. So you've used tools, maybe
searching perplexity for a title, marketing psychology,
articles from trustworthy sites, finding relevant
videos or podcast episodes, and collecting that
material. You've got your learning stack. Got
my library built. ready to learn right now you
bring all those articles transcripts maybe links
to videos notebook lm can process video transcripts
too apparently into notebook lm okay this is
where it can help you learn the material you
gathered now does it just summarize it all It
seems it can do more than just a simple summary.
The source says Notebook LM has built -in features
to generate things like a study guide and an
FAQ based only on your imported sources. Oh,
wow. You just click generate, apparently. So
it can actually structure the material I found,
not just spit back text? Yes. It's organizing
and summarizing the key concepts within your
specific learning materials. You could also ask
it for, say, a beginner -friendly overview using
simple language and real examples from the sources
you provided. That sounds way better than just
reading a dense textbook that might cover stuff
I don't need right now. I'm getting a personalized
summary based on the specific materials I curated.
Totally. You could ask it to summarize the top
marketing psychology principles and how to apply
them in marketing based on these sources. It
might explain principles like scarcity or anchoring
bias with examples pulled directly from the articles
or transcripts you imported. And I could ask.
Show me examples from these documents of how
companies use these psychological principles
in business, like using scarcity to boost sales,
if one of my articles discussed that specific
tactic. Uh -huh. And again, you could upload
your own website, let's say that child care business
site again, just for fun. Okay, bring the child
care site back in. And ask, how can these psychology
principles, as explained in these sources I'm
learning from, be applied to this website? It
might suggest highlighting limited enrollment
based on a scarcity principle explained in your
learning material and showing how that applies
specifically to a business like yours described
on your site. That's amazing. So it's not just
explaining concepts from my sources, but helping
me apply them immediately to my own context.
Yeah. That connects the learning to action directly
from the material I chose. That's powerful. It
really seems to be. It's leveraging AI to go
beyond just finding information, but helping
you analyze and synthesize insights from your
specific stack and maybe even apply them. Absolutely.
So wrapping this up then, the core idea is that
tools like Perplexity and Notebook LM, when used
together in this kind of workflow, they offer
a potentially powerful way to cut through that
information overload by focusing on your material.
Yes. Perplexity helps you broaden your search
and discover potential sources, cast that wide
net. But Notebook LM seems key for that focused,
hopefully hallucination -resistant analysis grounded
purely in the reports, articles, reviews, or
whatever documents you select and import. It
really highlights the synergy, right? Use a wide
net to gather, then take your specific catch
and do a deep dive into that. relying only on
what's in front of you in that tool. Exactly.
It's about cutting down the noise and getting
straight to the important stuff, grounded in
the materials you curated for your project or
your learning goal. So for you listening, think
about your own projects, maybe your learning
goals, maybe that stack of articles or reports
sitting on your desk right now. Where could this
kind of structured AI -assisted workflow, finding
broader context than analyzing your specific
documents for insights, help you cut down the
noise? get you straight to those key takeaways
or action items from the material you actually
care about. It feels like a potentially significant
shift in how we can approach information overload,
you know, allowing us to really leverage the
specific research we've already gathered or curated.
Totally. And maybe here's something to ponder
as these AI tools get smarter and more integrated
into our workflows. Okay. How does our own role
as the researcher or the learner really evolve?
Are we just becoming like users of these tools,
button pushers? Interesting question. Or does
leveraging them effectively, especially this
ability that the source highlights to ground
the analysis in our own chosen sources actually
make us. Better critical thinkers. You mean better
at curating quality sources in the first place.
Yeah, and better at synthesizing complex information,
asking the right questions to pull out the truly
valuable insights from our material. It forces
you to be more deliberate, perhaps, about what
you feed it and what you ask it. Maybe. Something
to think about as you explore this stuff yourself.