What if your AI didn't just tell you things?
What if it actually built them? Yeah, imagine
that. Imagine slashing weeks of work, having
an AI just deliver a completed business asset,
fully formed. Not just the answers, but actual
usable artifacts. It's a different level. Welcome
to the Deep Dive. Today, we're really digging
into a fascinating guide. It's called... Perplexity
Labs Guide, Five Business Use Cases and Prompting
Tips. And this isn't just about finding information
faster. It's about understanding how an AI tool,
specifically Perplexity Labs, can fundamentally
shift, shift from being just a research assistant
into what the guide calls a research -to -creation
engine. Which sounds pretty powerful. It does.
So we'll explore its unique spot in the whole
AI landscape, its quirks, its limitations, too.
Yeah, those are important. really impactful business
applications. Then we'll get into the precise
art of actually telling it what you need, the
prompting. The secret sauce. Our goal for you
today, to really uncover how this tool can save
massive amounts of time and honestly reshape
how businesses approach intelligence. And output.
It really feels like a significant leap, doesn't
it? Beyond just like finding information. How
so? Well, it's like moving from having this brilliant
librarian who, you know, gives you all the right
books to one who then takes those books and builds
you a completely finished functional model right
there based on her research. That's a fantastic
analogy. So this building layer. What does that
actually look like for someone using it? What's
the practical difference? The core difference
really is that it creates tangible stuff, functional
business assets, not just text. Got it. Tangible
assets. And to really grasp where Perplexity
Lab shines, it helps to understand where it fits
within the whole perplexity ecosystem. Totally.
Think of it like three tiers, a three -tiered
system. Each one's designed for a different level
of depth. Okay. Break that down for us. Tier
one. Right. So at the base, you have tier one,
basic search. This is probably the perplexity
most people know. Quick answers. Exactly. Simple
queries, you need an immediate answer, ideally
with sources. It's super fast, really efficient,
like asking that librarian for just one specific
fact. Boom. Okay, makes sense. Then tier two.
Then you step up to tier two. Deep research mode.
This is for when you need to really dig into
something complex. It performs a much more in
-depth analysis, pulls from a wider range of
sources, and then it generates a pretty detailed
research report. So like asking the librarian
to gather all the books on a subject and then
write up a summary? You got it. It's fantastic
for understanding a topic deeply. But the output,
it's still, you know, a static document, text
-based. Right. Which brings us to tier three.
And that brings us to tier three, Perplexity
Labs. This is the real game changer we're talking
about today. Okay. Labs takes that powerful research
engine from the other tiers and just adds this
building layer right on top. So it doesn't just
give you a report. It uses its research findings
to create something, a tangible, functional,
often interactive business asset. So it goes
beyond summarizing. Exactly. It's the only part
of their ecosystem that takes you all the way
from research, to a ready -to -use deliverable.
Okay, so choosing the right tier seems absolutely
crucial then. How should someone decide? When
do you use labs versus sticking with deep research?
It's actually pretty straightforward. Use labs
when you need something like an interactive dashboard,
maybe a functional tool. Like a calculator or
something. Yeah, or a professionally designed
presentation, you know, with charts and graphs
built in. Or even a working web asset like a
landing page. Basically anything that needs data
visualization combined with that underlying research.
And deep research. Stick with deep research when
all you need is the analysis itself. A literature
review, a text -based report, just gathering
information without needing a finished thing.
So boil it down for us, the main takeaway for
choosing. Labs builds, deep research analyzes.
Simple as that. Okay. But before you jump into
building all this cool stuff, there are some
critical things to know first. Prerequisites,
limitations. Right, absolutely. First off, labs
isn't free. It requires a perplexity pro plan.
And importantly, that pro plan comes with a usage
limit. Currently, it's 50 labs queries per month.
50? Okay, that's not unlimited. No. Which really
emphasizes the need to be thoughtful, strategic
with your prompts. You can't just mess around
endlessly. Right. Each query counts. Yeah. And
this next point, honestly, it's a big one. A
real security consideration. Oh. As of right
now, there is no way to revoke a shared link
to a lab's asset once you've created it. Wait,
no way at all? None that's documented. So if
you build, say, a dashboard with sensitive info
and you share that link, that link provides permanent
public access. Permanent public access. You absolutely
have to plan your sharing strategy very, very
carefully. It's like a permanent digital footprint
you can't erase. That 50 query limit and the
unrevocable link, I mean, that fundamentally
changes how you'd approach using this, doesn't
it? It really does. You know, I still wrestle
with prompt refinement myself sometimes, especially
knowing a credit is on the line beat. It forces
you to be super precise right from the start.
That makes sense. So how do you actually access
labs? Are there different ways? Yeah, there are
two main ways. First is direct labs access. Pretty
simple. You just click the little light bulb
icon and the perplexity interface takes you straight
there. Then you write your prompt telling it
what to research and what kind of asset to build.
Labs just handles both parts automatically. And
the second way? The second is called the research
to labs workflow. And this one's quite powerful,
actually. How does that work? You start your
session in the standard deep research mode first.
Do your initial information gathering there.
Okay, so you refine the research first. Exactly.
Once you're happy with that research foundation,
then you switch over to labs mode. The cool part
is the system keeps all the context from your
research phase and uses that information to build
your asset. So it gives you a bit more control
over that initial research part. Okay, that sounds
useful. But what about those limitations you
mentioned beyond the permanent links? Right.
Well, there are iteration challenges, unlike
some other AI tools where you can kind of chat
back and forth to refine something. Yeah, like
tweak this, change that color. Exactly. Perplexity
Labs tends to treat each significant change as
a whole new request. Oh. Which often means running
a new query and, yep, using another one of your
precious monthly credits if you want to alter
your dashboard after it's made. It's not really
designed for that conversational sculpting. Gotcha.
Less iterative, more one shot. Pretty much. And
it's worth repeating the security issue. Seriously,
that inability to revoke shared links is a huge
concern. So definitely don't use it for. Right.
Don't use perplexity labs for anything with private
client data, personal financial info, internal
secrets, basically any sensitive content you
wouldn't want potentially out there forever.
So not for highly confidential internal reports,
at least not yet. Not right now, no. It's just
too risky with those permanent links. So summing
that up, what's the biggest single caution you'd
give new users? For me, without a doubt, it's
the permanent public links for sensitive data.
Think very carefully before sharing. Okay. Good
advice. Mid -roll sponsor read. okay let's get
into the exciting part the actual applications
the guide highlights five specific ways this
tool can really transform business workflows
this is where it gets really interesting yeah
let's dive in first up use case one the prospect
research dashboard okay anybody in sales or marketing
knows prospect research can be Well, soul crushing
sometimes. Chuckle softly. Tell me about it.
It's tedious, fragmented. You're juggling expensive
databases, manually scraping LinkedIn, setting
up news alerts, trying to jam it all into some
messy spreadsheet. It's just incredibly inefficient.
Been there. So how does labs change that? With
labs, you use a single detailed prompt. Yeah.
Basically, you tell it to act as your personal
research team. Okay. And it builds this comprehensive,
interactive prospecting dashboard in minutes.
Minutes. Seriously. Yeah, minutes. Imagine prompting
it like this. Research high -growth DTC that's
direct consumer e -commerce brands in fashion
and beauty. Look for growth signals like new
product launches or recent funding rounds. Then
create an interactive dashboard with lead scoring,
detailed company profiles, contact info if possible,
and an industry breakdown. Aim for maybe 20 to
30 prospects. And what do you actually get back
from a prompt like that? You get a professional
-grade interactive dashboard. Usually in about
10 minutes. Wow. Yeah. It includes summary metrics,
a searchable database of prospects with company
details, maybe estimated revenue, contacts, detailed
profiles with source links. With sources. Nice.
Yep. Plus data visualization, like where they're
located or funding rounds. And often it even
throws in some AI -generated outreach templates
to get you started. That's incredible. The business
impact. Huge. It replaces easily 8 to 15 hours
of manual grunt work. That's like a 95%, maybe
more, time -saving. Goodness. Plus, it's way
better organized. It frees up sales teams to
actually sell, you know, do the high value stuff.
OK. Any pro tips for this one? Definitely. Always
double check the key data points. Maybe spot
check like. 10, 15 % of the entries, especially
contacts or revenue. Right. AI isn't perfect.
Exactly. And labs seems to work best data accuracy
wise for mid -market companies right now. So
be specific in your prompts about industries
and growth indicators. Got it. Okay. What's use
case two? Use case two, the high impact research
driven landing page. Ooh, interesting. Landing
pages are tough. They are. Traditional builders
give you templates, sure, but they don't give
you the messaging, the words that actually sell.
True. That usually needs deep market research,
customer research, which most businesses just
don't have time for. Right. But labs can uniquely
combine that deep research, competitive research,
customer research with the actual landing page
creation. So it writes the copy based on research.
Exactly. Resulting in a data backed asset. Picture
this prompt. OK, we're launching an email marketing
software as a service targeting small business
owners who run online courses. Research the messaging
of competitors like ConvertKit and ActiveCampaign.
Also, dig into customer feedback on sites like
G2, Capterra, maybe Reddit. Okay, so real user
opinions. Then create a professional one -page
landing page. Needs a data -driven headline,
maybe an interactive ROI calculator, a feature
comparison chart against those competitors, and
customer testimonials pulled directly from your
research findings. Keep the design modern, clean,
maybe blue and white. What makes that different
from just using a template builder? It's the
research integration. The AI isn't guessing.
It's analyzing real user reviews, looking at
how competitors position themselves, and crafting
messaging based on that. That's powerful. And
the output is professional. Interactive elements,
those data -backed testimonials. It can even
generate extra assets like those comparison charts
or pricing analysis on the side. And the business
impact. You're creating landing pages where the
messaging is almost guaranteed to resonate better.
because it's based on your target audience's
own words and a real market analysis. It takes
so much guesswork out of copywriting. Yeah, that's
huge. Okay, use case three. Use case three, the
real -time social media trend tracker. Ah, for
content creators, keeping up with trends is relentless.
Totally. You struggle to spot emerging trends,
and by the time you jump on one, it's often already
peaked or fizzling out. You miss the window.
So labs can create a live dashboard. Think of
it as your personal content strategy intelligence
hub. It's constantly monitoring the social media
landscape for you. Live dashboard? How does that
work? You'd prompt something like, create an
AI industry trend tracker. Monitor social media
discussions from the last 30 days covering AI
tools and startup news. Check across Twitter,
LinkedIn, Reddit. The dashboard needs to show
top trending topics, engagement metrics, maybe
sentiment analysis, which platform is leading
the discussion, and critically list common...
questions people are asking. OK, what does that
dashboard actually show you? It's dynamic. You
see topics gaining momentum, maybe with little
indicators. You see the overall sentiment, positive,
negative. And this is gold, a question mining
feature. It literally extracts the actual questions
real users are asking online. Oh, wow. That's
a content goldmine right there. Isn't it? The
business impact is transforming your content
strategy from just reacting to being proactive.
You spot trends early, jump on them intelligently,
and create content that directly answers the
questions your audience actually has. Data -driven
content planning. Very cool. What's number four?
Use case four. Competitive intelligence, but
with brand sentiment. Okay. Competitive analysis
usually feels a bit dry. Features, pricing. Exactly.
It's often just feature checklists. It misses
how the market feels about your competitors.
And that feeling, that sentiment, is where the
really big strategic opportunities often hide.
So Labs adds the feeling part. Precisely. It
combines that traditional product analysis, features,
pricing, et cetera, with real -time brand sentiment
pulled from social media, forums, review sites,
all in one place. Give me an example prompt.
Sure. Develop a competitive intelligence dashboard
for, say, Booking .com. Focus on Expedia and
Airbnb. Research and compare their features,
pricing models, loyalty programs. Also, track
brand sentiment for all three over the last 60
days. The dashboard should feature a side -by
-side comparison table, a sentiment score for
each brand with examples of positive and negative
comments, and maybe a market share visualization.
And the outcome of that? You get this rich, multi
-layered view. You don't just see that, you know,
competitor A has feature X. You also see that
competitor A's customers are constantly complaining
online about hidden fees. Ah, so you see their
weaknesses, their pain points. Exactly. It reveals
not just feature advantages, but perception advantages
and disadvantages, the business impact. It allows
for a much more sophisticated competitive strategy.
You can position your product not just based
on what it does, but on how it makes customers
feel, maybe directly addressing the pain points
driving competitive customers crazy. It's like
knowing the emotional battlefield. That's a great
way to put it. Okay, the final one, use case
five. And this one. This one's pretty impressive.
Use case five. The go -to -market strategy presentation.
OK. GTM strategies. Those take ages to put together.
Ages. Creating a comprehensive GTM strategy deck
that's your whole plan for launching a new product
or service, right? It's usually a massive undertaking.
Weeks, sometimes months of research, data analysis,
financial modeling, slide design. A huge amount
of work. Labs. Well, Labs does almost all the
heavy lifting. It condenses potentially weeks
of that work into minutes. Okay. I need to hear
this prompt. Right. So something like, develop
a GTM strategy presentation for a new online
learning platform. Let's say it focuses on AI
skills for professionals. Research the market
size and growth potential. And what does Labs
actually produce from that? professionally designed
PowerPoint or Google Slides presentation, usually
in about 15 to 20 minutes. 15 to 20 minutes for
a whole GTM deck. Yeah. It's polished. It has
charts, graphs, data -driven content, all backed
by the sources it used. It includes all the key
GTM elements you'd expect, market opportunity
analysis, you know, TAM, SAM, MRM, total addressable
market, serviceable available market, serviceable
obtainable market. Basically, how big the pie
is and how much you can realistically get. Right.
Gives you a competitive differentiation matrix,
detailed customer personas based on its research,
even a suggested KPI framework, key performance
indicators to track success. Honestly, it might
be the most powerful example of pure efficiency
gain here. Whoa, moment of wonder. I mean, imagine
creating a professional GTM deck in 20 minutes.
That's practically a superpower, especially for
startups or even established businesses trying
to move faster. It really is. Condensing two,
three weeks of intense work into a 20 -minute
automated process. The agility that gives you.
Being able to test and refine strategies at speeds
that were just unimaginable before. It's kind
of mind -blowing. Looking at all five, which
one seems like it would have the most immediate
widespread impact for many businesses listening?
Oof, tough call. They're all strong. But I think
probably the GTM strategy. Just because the time
savings are so enormous on such a critical, complex
task. Yeah, hard to argue with saving weeks of
work. Okay, so we've seen these amazing use cases.
But the guide stresses that the secret to unlocking
these high -quality outputs, it isn't just the
tool itself, it's the instructions you give it.
Absolutely. Professional results demand an investment
in crafting your prompt. It's not magic. It's
instruction. So how do you craft a great prompt
for labs? The guide mentions three pillars. That's
right. The three pillars of a great prompt. First
is comprehensive context setting. Meaning? Meaning
you need to clearly tell the AI what role it
should play. Like you are a senior market research
analyst working for a venture capital firm. Give
it a job title. Exactly. And provide specific
business context, your industry, your target
market, your goals for this asset. Give it a
persona and a clear mission. Okay. Pillar one,
context. What's pillar two? Pillar two is detailed
output specification. You have to be crystal
clear about the deliverable. Be specific. Super
specific. Don't just say, make a report. Say,
Create an interactive dashboard or generate a
10 -slide executive presentation in PowerPoint
format. Specify the functionality you need. The
dashboard must include a searchable table filtered
by industry. Even specify design requirements.
Use a consulting -grade professional design or
adhere to our brand color scheme of blue and
white. The more detail, the less guesswork for
the AI. Got it. Context -specific output. Pillar
three. Pillar three is clear research focus.
You need to guide its research. Specify timeframes.
Only consider data from the last 30 days. Indicate
source preferences, focus on social media conversations,
or prioritize reviews from G2 and Keptera. And
definitely name the specific competitors you
wanted to analyze. This helps it zero in on precisely
the information you need. rather than boiling
the ocean. Okay, context, output specs, research
focus. And the guide mentioned something about
demanding quality. Yeah, this is a great tip.
You can dramatically improve the output quality
by including specific quality indicators right
there in your prompt. Instead of just hoping.
Right. Don't hope, demand quality. Use phrases
like, create a deliverable with a consulting
-grade professional design. Or, the final output
should be of executive presentation quality.
Ah, setting the standard up front. Exactly. The
dashboard must have full interactive functionality.
The landing page should follow modern web design
standards. All claims must be supported by data
-driven visualizations. You're telling it the
benchmark it needs to hit. Why is being so detailed
with prompting especially crucial here, maybe
more than with other AI tools, given the credit
limits and iteration challenges? It's really
about demanding those specific, high -quality
deliverables up front. You want to maximize the
chance of getting what you need on the first
try without burning through those limited credits
on revisions. Precision minimizes waste. Makes
perfect sense. So let's talk optimization and
the bottom line, the business impact. How do
you get the absolute maximum value from labs?
Well, building on the prompting, number one is
strategic prompt planning. Seriously, spend those
extra few minutes crafting a really detailed
prompt before you hit generate and use a credit.
That upfront investment pays off massively in
the output quality. Measure twice, cut once,
or prompt once, ideally. Duckles, exactly. Precision
in, perfection out, hopefully. Second, always
maintain a data verification workflow. Fact checking.
Yep. Especially for key data points, numbers,
contacts, anything you're going to use externally
or base major decisions on, always sanity check
it. Good practice for any AI output, really.
Absolutely. And third, have an asset management
strategy. When labs create something, a presentation,
dashboard code, beta files. Download everything.
Local copies. Yeah, for offline access, backup,
maybe integrating it into other workflows or
tools. And yes, I know we sound like a broken
record, but to be extremely strategic about who
gets those permanent share links, maybe don't
share them at all if possible, just use the downloaded
files. Right. Manage the assets, manage the links.
Okay, the business impact. Let's quantify this
ROI. The time saving seems central. They're staggering.
It's not just incremental efficiency. It's a
fundamental shift in how quickly you can operate.
Give us those numbers again. Okay. Prospect research.
Traditionally, maybe 8 to 15 hours, right, with
labs, potentially 10 minutes. That's easily a
95 % time saving or more. 95%. Wow. And the go
-to -market strategy. Two, three weeks of intense
work. Labs can draft that initial comprehensive
deck in maybe 20 minutes. That's like a 98 %
plus time saving. That's almost unbelievable.
It allows businesses to just test more ideas.
Respond to market shifts way faster. And crucially,
it lets you focus your human talent, your expensive
creative people on high value strategic thinking,
interpretation, decision making. Instead of just
low value data gathering and slide formatting.
Leveraging human creativity where it actually
counts the most. That's the goal. So stripping
it all back, what's the single biggest benefit
of this incredible speed? I'd say unprecedented
business agility coupled with a much sharper
strategic focus. Move faster, think smarter.
Okay, let's wrap this up. Big picture takeaway
for everyone listening. Perplexity Labs isn't
just another AI that answers your questions.
It's fundamentally different. How so? It's an
AI that builds. It creates professional, actionable
business assets for you. It truly bridges that
gap, doesn't it? Between just raw research and
actually having something tangible, something
created. Automating potentially weeks of work
into mere minutes. It really does. And the key,
the absolute key to unlocking all that power.
Let me guess, the prompt. Precise, well -crafted
prompts. It really is all in the ask. So here's
a final thought to leave you with. This profound
shift we're seeing, moving from AI doing just
analysis to doing active creation, it implies
something significant. What's that? It means
the competitive advantage, certainly in the coming
years, will likely go to those individuals and
those businesses who master these new research
to creation workflows first. So the question
becomes... Will you be leading that transformation
or will you be trying to catch up to it? It's
definitely an exciting time to be building things.
Lots to think about. Absolutely. If this deep
dive sparked your curiosity, maybe showed you
some new ways to approach your work, remember
to explore our other deep dives on similar topics.
Lots more to uncover. Until next time, keep learning.
And keep building. Outro music.