You know, it strikes me that we collectively
misunderstand the tool we're holding. We look
at ChatGPT, and we treat it like a poster. You
put bread in, you get toast out. It's a transactional,
single -use interaction. But looking at the research
we have today, that is fundamentally the wrong
mental model. Completely wrong. The real power
isn't in the one -off question. It's in treating
the AI like a system. Yes. A system that remembers,
a system that learns, and a system that actually
organizes your chaotic thoughts into something
coherent. That's the disconnect right there.
We treat it like a magic eight ball. Yeah. Shake
it, get an answer, put it down. But the source
material we're diving into today, systematize
your workflow. 15 advanced chat GPT hacks. It
just flips that script. It does. The core argument
here is simple. If you find yourself typing the
same context twice, You have already failed.
Welcome to the Deep Drive. It is good to have
you here. Today, we are going to try and move
past the viral tricks. Please! We aren't doing
write me a poem about a cat in the style of Shakespeare.
Well, laughing. No, we are not. We want to get
into the mechanics of building what the source
calls a personal workflow engine. We have a roadmap
today that covers setting up memory, automation,
project structures, and some truly advanced decision
-making tools. This is the difference between
playing with a toy and hiring a chief of staff.
And honestly, it starts with the thing most of
us get completely wrong. What's that? Identity.
Right. The foundation. The source calls this
teaching chat GPT your writing style. I think
this resonates because, well, how often do we
find ourselves typing, make this sound more professional,
or make this shorter? Stop using the word Dell.
Every single day, it's prompt drift. You spend
half your energy fighting the AI's default voice,
which is usually that overly cheerful corporate
customer service robot. It is exhausting. But
is it fixable? Because I feel like I fix it in
one chat, and then I open a new one, and we are
back to square one. That's exactly what the source
addresses. It gives us a specific mechanism to
fix this permanently. OK. And the key is, don't
describe your style. Show it. Show, don't tell.
You don't say, I write casually. You take an
email. You actually sent. when you're proud of,
paste it in, and use a very specific prompt to
lock it in. Let's look at that prompt, because
phrasing seems important. It is. You say, analyze
this for tone, style, format, and reading level.
You let the AI break you down. It effectively
deconstructs your linguistic DNA. And then, this
is the kicker, you tell it to store that memory
for a specific context. Like, remember, this
is how I write to my team. That specific context
part seems critical. It's everything. Because
the way I write a script for this deep dive is
very different from how I write an email to a
confused accountant. Precisely. The source emphasizes
one style per context. You can have a client
email persona, a Twitter thread persona, and
a strategic memo persona. All stored. All stored
in memory. So next time, you just draft an email
to the team and it pulls the team mask off the
shelf. No more manual correction. I have to admit,
I... I still struggle with this. I find myself
manually correcting the tone in almost every
session because I haven't taken the five minutes
to set this up. It feels, I don't know, almost
lazy on my part not to do it. It's not just you.
The source mentions that personalization settings
are the features most people skip. They're hidden
away. They're hidden in the settings menu, but
they act as a global baseline. You can go in
there and set a default about you. your role,
your experience level, your preference for no
fluff answers. So you're giving the AI a permanent
dossier on who you are. Exactly. So you don't
have to introduce yourself every time you walk
into the room. So does this mean we stop treating
it like a generic robot? Yes. It becomes a mirror
of your specific communication style. It stops
sounding like an AI and starts sounding like
you on your best day. That is a compelling thought.
But to make that work, you need a container,
right? You can't just have all these different
personas floating around in a void. Right. This
brings us to the second pillar of our discussion.
Structure. Specifically, projects. Projects are
huge. If you're still using the default chat
history, you know, that infinite scroll of random
conversations on the left side of the screen.
Which is most people. It's most people. You are
living in a messy house. The source describes
projects as folders with a brain. Folders with
a brain. Break that down for me. Think about
your physical desk. You wouldn't stack your tax
returns, your creative writing journal, and your
grocery list in one pile, would you? I mean,
I might, but I shouldn't. Exactly. Right now,
most people's chat history is that pile. Projects
let you create distinct workspaces. Okay. One
project for client work, one for creative writing,
one for learning Python. And here's the key.
Each project has its own custom instructions
and its own files. And there is a specific setting
here that the source flags as credible project
-only memory. Yes. This is the safety lock. By
default, chat GPT's memory can bleed across conversations.
You do not want the casual, slang -filled tone
you used in your creative writing project to
suddenly show up. In your corporate strategy
project. That would be a career -limiting move.
It would be awkward, to say the least. Project
-only memory isolates the context. It ensures
that what happens in the project stays in the
project. Right. It's essential for privacy too,
especially if you're uploading sensitive client
docs. It reminds me of the concept of context
switching in human psychology. It takes energy
to switch from one task to another. By creating
these projects, you're effectively offloading
that cognitive load onto the machine. That's
the perfect analogy. You're building distinct
rooms for distinct modes of thinking. That's
it. And sometimes, you don't even want a room.
Sometimes you just want a sandbox. The source
mentions temporary chats for this. Right, the
incognito mode of AI. Basically. These are chats
that don't save, don't train the model, and don't
create memories. Okay, so what's that for? It's
perfect for when you need to ask a dumb question,
or test a prompt, or run a sensitive role play,
and you just want the slate wiped clean immediately
after. It keeps your main memory banks from getting
polluted with garbage. Why is the isolation of
memory so critical here? It's context hygiene.
You don't want your casual chats leaking into
professional strategy. You keep the signal to
noise ratio high. Cleanliness is next to godliness,
even in code. Right. Now let's move to something
that I think changes the dynamic of the relationship
entirely. We're talking about automation. Yeah.
The shift from passive to active. This is where
it gets fun. Most of us think of Chat GPT as
a tool we have to pick up and use, but the source
points out that you can flip that dynamic. You
can set up a daily reminder system. Give us an
example of how that works practically because
my phone already has reminders. True, but your
phone just pings you with a label. Chat GPT can
perform a task. A task. You can tell it. Every
weekday at 8 a .m. send me three important AI
updates focused on OpenAI and Google. It creates
a background task. It runs whether you open the
app or not. So it pushes the information to me.
It pings you with the briefing. That is a subtle
but profound shift. It's no longer waiting for
input. It is initiating the interaction. It's
massive. And you can scale this up with what
the source calls the company knowledge base.
Right, for teams. If you're on a team plan, you
can connect your Google Drive, your Notion, your
calendars. So instead of asking chat GPT to search
the web, You're asking it, what did we decide
in last week's marketing meeting? And it's pulling
the answer from your actual internal doc. From
your stuff. And there's also this mention of
apps and connectors using the at symbol. I've
seen that. Yeah. This is the app layer. You type
at in the chat and you can call up external tools.
You can summon Khan Academy for a math explanation
or a design tool to generate a vector image right
there in the flow of conversation. So no more
tap switching. It's orchestration. It's fascinating.
Imagine scaling this. Waking up to a personalized
briefing created by an agent that ran through
your internal docs and the external web while
you slept. It feels like we are inching closer
to that sci -fi vision of a digital butler. Ideally
a butler that doesn't judge my calendar management.
But yes, that's the dream. So if we look at this
automation layer, it stops being passive. Right.
It transitions from a tool you hold to an assistant
that nudges you. It becomes proactive. I want
to pause for a moment to consider the implications
of that. But first, let's take a quick break.
We are back. We've talked about identity, structure,
and automation. Now I want to get into the act
of creation itself. Yes. The source mentions
advanced creation, specifically dealing with
how we generate output images, code, mini apps.
This is for the builders. The first hack here
is model switching. inside custom GPTs. This
is a bit technical, but super important. Let's
break it down. People build these custom assistants,
maybe a writing coach, GPT, but they built it
six months ago. It's running on an old brain.
And the source said you can swap that brain out.
Exactly. You can go into the settings of an old
GPT and force it to use the new thinking models
like 01 for deep reasoning. It's like taking
a vintage car and dropping a Ferrari engine inside.
That's it. You keep the interface, but the performance
skyrockets. Speaking of performance, the image
generation workflow described here seems much
faster than the usual method. It's the batch
images hack. Usually people prompt, make a logo.
Wait, make it blue. Wait. It's so slow. Yeah,
it's iterative. The source says do it in parallel?
Tell it. Create a hero image for a sauce website.
Also create a dark mode version and a minimalist
thumbnail version. So you get three variations
instantly. And you can spot the direction you
like immediately without waiting for three separate
failures. That brings us to canvas mode. This
is something I've seen pop up recently. It seems
to be more than just a chat window, but less
than a code editor. Canvas is incredible. It's
essentially a prototyping environment. If you
ask ChatGPT to build a to -do list app and you're
in Canvas mode, it doesn't just write the code
snippet. It renders a preview. You can click
the buttons. You can check the boxes. So you
are building software without writing a line
of code. You're building interfaces. Calculators,
planners, simple interactive tools. You describe
it, it appears. What does Canvas mode actually
represent for a non -coder? Instant prototyping.
It turns an abstract idea into a clickable reality
in seconds. It removes the fear of the blank
page because you can see the thing working immediately.
That removal of friction is the theme here, but
I want to go deeper. Let's do it. The final section
of our roadmap covers deep thinking. We are moving
beyond simple tasks to research, strategy, and
complex decision making. This is where the power
users live. The source highlights deep research.
combined with your own data. OK. Now, deep research
is an agent that goes out, reads dozens of websites,
and compiles a report. That's great. But the
magic happens when you tell it to cross -reference
that web data with your uploaded internal strategy
documents. So it's validating your internal assumptions
against the external reality of the market. Precisely.
It's a reality check machine. And speaking of
reality checks, there's this decision simulator.
I love this one. How does that work? You're stuck
on a big decision. Maybe, should we? pivot to
enterprise clients. Instead of asking for advice,
which can be vague, you ask for a simulation.
A simulation. You say, simulate this decision
from three perspectives. My customer, a competitor.
and a skeptical investor. A skeptical investor.
That's a useful perspective to have on demand.
It is. And the AI role plays all three. It predicts
the friction points, the counter moves by the
competitor, the doubts of the investor. It helps
you see your blind spots. It's not a crystal
ball. No, but it clarifies the risk. That is
powerful. There is also a tool mentioned for
documentation, the SOP generator. But the method
described is conversational. This is the interview
mode hack. Yeah. Most people hate writing standard
operating procedures, SOPs. It's boring. It's
so boring. So instead of writing it, you tell
chat GPT. Ask me clarifying questions one by
one about how I do this task and then turn my
answers into a detailed SOP. So it interviews
you about your own job. Yes. You just brain dump
the answers verbally or in quick text and it
structures the chaos into a formal document.
It captures your tacit knowledge. The stuff you
do without thinking and writes it down for you.
And finally, there is the concept of the knowledge
graph. This feels like a long -term project.
This is for the lifelong learners. Yeah. You
create a project called Thinking and Research.
You dump your book notes, your random ideas,
your article summaries into it over months. Okay.
And periodically you ask it, what patterns are
emerging in my thinking or connect this new idea
to things I wrote three months ago? It's looking
for the connective tissue in your own intellect.
It is. It's finding the threads you forgot you
were weaving. What is the ultimate value of the
knowledge graph approach? Synthesis. It finds
connections between your ideas that you might
have forgotten. It stops your knowledge from
being just a pile of notes and turns it into
a web of understanding. Synthesis. That is the
goal. We have covered a lot of ground here. We
have. From establishing an identity to building
a structure, automating the mundane, prototyping
ideas, and finally, using the machine to deepen
our own thinking. It's a lot. And the source
makes a great point in the big idea recap. The
people getting the most value aren't the ones
writing the perfect complex super prompt. No.
They're the ones building systems. It is a shift
from chat bot, which is transactional, to operating
system, which is cumulative. Right. And the advice
is don't try to do all 15 of these things tomorrow.
You will burn out. Just pick one. Pick one. Maybe
just set up the project only memory so your work
stays clean. Or maybe just try the writing style
hack so you stop fighting the tone. Stack them
gradually? Exactly. Lego blocks, not a monolith.
So here is our challenge to you, the listener.
Pick one. Try the decision simulator on a choice
you're avoiding making. Or try the writing style
hack and see if it feels like looking in a mirror.
And honestly, try the SOP generator if you hate
documentation. It's a lifesaver. It really sounds
like it. You know, as we close, I'm left with
this thought about memory. We're outsourcing
not just our tasks, but the retention of our
context. If the AI remembers our style, our past
projects, our patterns of thought better than
we do, it changes how we view our own past work.
It's no longer something we leave behind. It's
something that actively informs what we do next.
It makes everything we've ever done available
to us right now. That's a superpower. A superpower
indeed. Thank you for listening to this deep
dive. Catch you on the next one.