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.