There is a trap that almost everyone falls into
when they first start using these modern AI tools.
I find myself doing it, even though I know better.
It's the Google plus chat GPT trap. You treat
the search bar like a slot machine. You pull
the lever, you type in a generic question, and
you just sort of hope the algorithm spits out
gold. Right. It's the pull and pray method. Exactly.
It's passive. And honestly, it is how we have
been trained to use the Internet for 20 years.
But according to the research we are looking
at today, if that is how you are using perplexity
AI, you are effectively driving a Ferrari in
first gear. You are utilizing maybe. 10 of its
actual power which is wild because the other
90 isn't just about getting slightly smarter
answers yeah it's about changing the fundamental
relationship you have with the tool yeah it stops
being a search engine you visit and starts becoming
a research system that you design that is the
phrase that stuck out to me intentional system
design so welcome to the deep dive today we are
unpacking precision research we're going to look
at how to master the advanced workflows of perplexity
ai and we are moving past the basics here Know
how to sign up tutorials. This is for the person
who wants to go from casual user to power user.
We have a roadmap for this conversation. We are
going to cover five specific shifts in workflow.
First, we'll talk about controlling where the
AI looks using search operators. Second, we'll
look at automating the busy work with slash commands
and recurring tasks. Which is a massive time
saver for anyone who hates repetitive typing.
Third, we'll discuss building a persistent brain
using spaces. Fourth, selecting the right mode
and model for the specific job because they aren't
all the same. And finally, we'll get into advanced
prompting techniques like chaining and perspectives.
It's a full stack for information processing.
It is. So let's start with the first concept,
the art of constraint. The source material makes
a point here that feels counterintuitive. It
says that quality gains don't come from expanding
your search, but from limiting it. Yeah. This
is the biggest mental hurdle. We are trained
to think that big data means more data. If I
ask a question, I want the AI to scour the entire
Internet, right? Why wouldn't I? Theoretically,
yes. More sources should equal a better answer.
But practically, no. Because the Internet is
noisy. It is a dumpster fire of conflicting information.
If you ask a technical question, let's say you're
trying to figure out a specific nuance in a Python
library or a tax law change, and you let the
AI look everywhere. What happens? It pulls from
everywhere. It stitches together an answer from
the official documentation, sure. But it also
pulls from three angry Reddit threads from 2019,
a marketing blog trying to sell you a course,
and maybe a YouTube comment section. It flattens
the hierarchy of truth. A random comment weighs
as much as the documentation. Exactly. It creates
a Frankenstein answer. It might be plausible,
but it's not precise. So the source highlights
the power of the site operator. This is the most
useful tool in your kit. Walk us through that.
How does it work in practice? It's incredibly
simple. You type your query, and then you add
site, followed by the domain you trust. So site
.nasa .gov or site .apple .com. So you're forcing
the AI to web blinders. You're forcing it to
ignore the noise. Let's say you want to understand
how the new Stripe API handles recurring billing.
If you just ask perplexity, you'll get a mix
of opinions. But if you type your question and
add site .docs .stripe .com, you are guaranteeing
that the answer isn't coming from a confused
developer on a forum. It eliminates opinions.
We usually think more data is better, but here,
less is actually more. Why is exclusion so powerful
in this context? Because exclusion removes the
noise. forcing the AI to focus only on trusted
truth. The source also mentions time -based operators
after and before. I usually ignore these in Google
searches. Why are they critical here? Because
of how fast the world moves, especially in tech
or news. If you ask about the best open source
LLM right now. And perplexity pulls an article
from November 2023. That answer is worse than
useless. It's actively misleading. The field
moves too fast. Exactly. By adding after plot
2024, you ensure freshness. You aren't reading
history. You're reading news. And my personal
favorite from the deep dive, filetype .pdf. Ah,
the academic filter. It's the anti -SEO filter.
If you want deep research on a medical topic
or economic trends, you don't want the top 10
Google results. Those are all SEO optimized marketing
fluff. You want white papers. You want government
filings. Adding filetype .pdf tells Perplexity,
don't show me the blog posts. Show me the receipts.
That brings us to our second segment, systemizing
the workflow. This is about moving from a one
-off tool to a daily assistant. Right. The goal
is to stop typing the same things over and over
again. The source talks about slash commands.
Now, for people who use tools like Slack or Notion,
this might feel familiar. But how does it apply
to a search engine? Think of slash commands as
saved prompt templates. We all have those tasks
we do repeatedly. Maybe you take messy notes
and you always want them formatted into a clean,
bulleted list. Or you take a complex article
and you always want a three -sentence summary.
Right. And usually I type out, please take this
text and summarize it into three sentences every
single time. Exactly. And if you're tired, maybe
you forget to say three sentences or you forget
to say keep it professional. The output becomes
inconsistent. With the slash command, you save
that instruction once. You call it summary. So
I just type slash summary. You type in the box,
pick summary, paste your text, and hit enter.
It executes the exact same prompt structure every
time. It guarantees consistency. It removes the
cognitive load of having to manage the AI. You
just trigger the workflow. I have to admit, I
still type the same prompt structure manually
every morning. I haven't automated my own habits
yet. It feels like one of those things where
you know you should do it, but you just keep
doing it the hard way because it feels faster
in the moment. It's a classic, I'm too busy chopping
wood to sharpen the axe problem. But the source
points out that slash commands are currently
a web and desktop feature. But once you set them
up, it saves hours over a month. But slash commands
still require you to be there typing. The source
mentions a pro feature called recurring tasks.
This sounded fascinating. This is where we cross
the line from tool to agent. A recurring task
is a search that runs itself. Without me. Completely
without you. Yeah. Let's say you need to track
a competitor's pricing or you want to know if
there's any new news about a specific stock.
You set up a prompt, you tell perplexity, run
this every day at 8 a .m., and you walk away.
So I don't open the app. No. You wake up, you
check your notifications, and the briefing is
there. It shifts to the dynamic. You aren't checking
for updates. You are receiving them. So this
essentially turns the AI from a tool you pick
up into a worker that clocks in for you. Exactly.
It works in the background, so you wake up to
answers, not empty search bars. Let's move to
the third segment. This is about memory and context.
The source calls this spaces. This solves the
amnesia problem. The amnesia problem. Standard
AI chats are ephemeral. You have a great conversation,
you close the tab, and it's gone. Or it's buried
in history. If you start a new chat, the AI has
forgotten everything you just discussed. It doesn't
know who you are or what you're working on. So
how do spaces fix that? A space is a container.
It's a persistent environment. You can create
a space called Project Alpha or Marketing Strategy.
And here is the killer feature, knowledge injection.
Knowledge injection? Sounds sci -fi? It kind
of is. You can upload files, PDFs, spreadsheets,
massive internal documents into that space. Right.
And you can tell perplexity. Everything in this
space must reference these files. So if I upload
my company's brand guidelines into a space and
then I ask it to write a blog post inside that
space. It'll write that blog post using your
brand voice, adhering to your guidelines without
you having to remind it. It remembers. The source
also mentioned something about isolation. This
is huge for enterprise or deep research. You
can toggle a setting in a space that says no
web. No web, but it's a search engine. But sometimes
you don't want the web. If you are analyzing
confidential internal financial reports, you
don't want the AI hallucinating numbers from
a Yahoo Finance article. You want to look only
at the files you uploaded. By turning off the
web, you force it to ground its reality entirely
in your data. Whoa. Imagine the scale of that.
You're basically building a custom brain that
only knows what you want it to know. You strip
away the rest of the world and just focus on
your signal. That's the power. It becomes a personalized
knowledge base that actually remembers your specific
context. Does this change the relationship with
the AI from a search engine to something else?
Yes, it becomes a personalized knowledge base
that actually remembers your specific context.
We have so much more to cover, including the
different brains you can swap in and out and
the Socratic method of prompting. But first,
a quick break. We are back. We're deep diving
into precision research with Perplexity AI. We've
covered operators, automation, and spaces. Now
let's talk about the engine under the hood, or
rather, the engines. Right. This is segment four,
the brains and the modes. The source makes a
distinction here that I think confuses a lot
of people. Perplexity isn't one single AI model.
It's a wrapper. It's an interface layer. Underneath,
you can actually choose which brain is doing
the thinking. And this is critical because different
models have different personalities. Let's run
through them. What are the options and when should
we use them? So you have the default model. It's
balanced. It's fast. It's great for 80 % of things.
But then you have Claude. From Anthropic. Right.
Claude is generally calmer. more structured,
and significantly better at writing. If you want
a blog post or a nuanced explanation, you switch
the model to Claude. And Gemini. That's Google's
model. It shines with mixed inputs if you're
analyzing images or need fast multimodal processing.
And then, of course, GPT -4 from OpenAI. GPT
is the logician. If you have a complex reasoning
problem, a math puzzle, or a coding task, GPT
tends to be better at following step -by -step
instructions without getting creative in the
wrong way. the source suggests a testing method
for this yeah and it's something everyone should
do at least once take a complex prompt run it
through the default model then switch to quad
and run the exact same prompt then gpt the answers
will be different not just in style but in substance
you'll start to develop an intuition for which
brain fits your problem now alongside the model
there is the mode the mode is the method how
does the ai go about finding the answer The source
lists three main ones. Search, research, and
labs. Search mode is what we're used to. Speed
over depth. What is the capital of France? Who
won the game last night? It grabs a few sources,
synthesizes them. Boom. Done in seconds. But
then there is research mode. The source calls
this the powerhouse. Research mode is fascinating.
It's slower by design. When you ask a question
in research mode, perplexity doesn't just answer
it. It breaks your question down into sub -questions.
Can you give us an example? Sure. If you ask,
what is the impact of the new tax law on small
businesses? In search mode, it finds a summary.
In research mode, it acts like a project manager.
It says, OK, I need to look up the tax law text.
I need to look up analysis for retail businesses.
I need to look up analysis for tech startups.
I need to look at projected economic impacts.
It runs four or five parallel searches, reads
10 or 20 sources, and then writes a comprehensive
report. So it synthesizes. It synthesizes. Yeah.
It's for when you need a report, not just a fact.
Labs is for artifacts. If you need it to write
code, generate a chart, or create a specific
document structure, labs acts more like an agent
developer. It seems most frustration comes from
asking a logic model to do creative work, or
vice versa. How do we build the intuition for
which to pick? Trial and error. Test the same
prompt on two models. The winner becomes your
default for that task. Let's move to our final
segment, advanced reasoning. This is where we
stop looking at features and start looking at
how we talk to the machine. The source discusses
chaining. This is the antidote to the God prompt.
The God prompt. You know, the one you try to
cram everything into one massive paragraph. Please
explain quantum physics compared to string theory.
Tell me which one is better for a sci -fi novel
and write the opening chapter. Guilty. We all
are. But the AI gets overwhelmed. It tried to
do everything at once and gives you a shallow
version of everything. Chaining is about breaking
it down into a conversation. Step one, clarify.
What are the main theories of quantum physics?
Get the landscape. Step two, go deeper. Explain
string theory in detail. Step three, compare.
Create a table comparing quantum loop gravity
and string theory. Step four, apply, which offers
better plot devices for a time travel story.
By the time you get to step four. The AI has
all the context from steps one, two, and three.
You aren't asking it to guess. You've built a
foundation of facts together. The reasoning becomes
much sharper because you forced it to show its
work along the way. The source also mentions
perspective prompts. This is a simple trick,
but effective. Default AI answers are neutral.
They try to be objective, which often means bland.
On the one hand, on the other hand. Exactly.
But if you say, answer this as a cynical SaaS
founder or answer this as a cautious academic
researcher, the output changes completely. How
so? The SaaS founder will prioritize speed, profit,
and growth. The academic will prioritize accuracy,
citations, and caveats. The facts might be the
same, but the wisdom is different. You get to
view the data through a specific lens. Finally,
there's a mention of Notebook LM as a companion
tool. We've talked about Notebook LM on other
deep dives, but how does it fit here? Perplexity
is for hunting. It's for finding the information.
Notebook LM is for gathering. You don't want
to lose that great research you just did. So
the workflow is hunt in perplexity, find the
gold nuggets, and copy -paste them into Notebook
LM. That becomes your permanent library that
you can query later. This reminds me of the Socratic
method. We aren't just demanding answers. We
are teaching it how to think about the problem.
Right. You're building a ladder of logic, stepping
up from facts to wisdom. So we've covered a lot
of ground today. Let's try to pull this all together
in a Big Idea recap. Sure. The big idea is that
perplexity is not just a better Google. It is
a research engine that requires a pilot. If we
break it down to the key takeaways. First, constraints.
Use operators like sight. And after, to filter
out the garbage. Quality comes from exclusion.
Second, automation. Use slash commands for templates
and recurring tasks for things that happen on
a schedule. Make the machine work for you. Third,
context. Use spaces to ground the AI in your
own data and files. Give it a memory. Fourth,
selection. Be intentional about the model, Claude
versus GPT, and the mode, search versus research.
Don't use a hammer to turn a screw. And finally,
reasoning. Chain your questions. Don't ask for
miracles in one prompt. Guide the thinking process.
The source ends with a really provocative thought.
It says that once you master these tools, the
AI stops feeling impressive and starts feeling
reliable. I love that. It implies that the wow
factor is actually a distraction. It is. When
technology is new. We want to be dazzled. Oh,
look, it wrote a poem. But when you're actually
working, you don't want to be dazzled. You want
to be supported. You want the tool to disappear.
Reliability is boring. Reliability is boring.
And boring is productive. If you use these workflows,
if you set up the tasks, if you build the spaces,
perplexity becomes like electricity. You don't
marvel at the light switch. You just expect the
light to turn on. That is where we want to get
to. That is the goal, to reach a point where
you just have the knowledge you need when you
need it. Thank you for listening to this deep
dive. Go try out a recurring task this week.
See if you can automate just one piece of your
daily curiosity. It's worth it. We'll see you
next time.