Most people use AI like a fancy search box. We
type a simple question, get an OK answer, and
we move on. It's pretty transactional. But what
separates that casual user from someone who's
operating in the top 1 %? It's not just asking
better questions. It's teaching the AI how to
think and how to build for you. And that's really
the core difference, isn't it? The AI is built
for speed, maybe not always for perfection. So
welcome to the Deep Dive. Today yeah, we're talking
about transforming chat GPT moving it from just
a quick answer machine into well a systematic
personalized thinking partner We've looked at
a whole stack of power user guides really dug
in and we're gonna distill it down to three core
shifts you could make first is making the AI
self -correct second giving it a kind of long
-term memory and Finally training it to be your
specialist coach right our mission here is pretty
simple We want to move you from just asking for
those quick answers to actually building customized
Autonomous systems systems that can save you
out every single week. Okay, so let's unpack
that first shift. Systemizing the output. We
kind of know the first answer Chet GPT gives.
Well, it's often just average, right? It's the
fastest thing it can generate. And sometimes
speed isn't what you need. It can be the enemy
of quality. Exactly. It defaults to speed. So
the first big strategy here is something called
the self -check method. And this basically forces
the AI to do better work by building critique.
right into the prompt itself. Yeah, it's a prompt
trick, basically. You don't need anything fancy.
You're telling the AI how to think before you
tell it what to write. It's like we build a little
internal review process, but it's hidden from
you, the user. OK, so how does that work in practice?
How do you build that internal critique into
just one prompt? What are the steps? You set
up a four -step sequence. So step one, think
inside. It outlines what makes a good response.
But, and this is key, it doesn't show you this
thinking. Step two is write draft one. which
also stays hidden. That's usually the fast kind
of average answer. And the magic happens in step
three, right? Check and fix. This is where you
tell it, OK, critique draft one against those
rules you thought about. Find the weak spots
and fix them. Exactly. And then finally, the
AI only shows you the superior draft two. And
this works because it forces the AI to kind of
slow down, dedicate resources to self -reflection.
It catches its own mistakes before you do. So
the final output is just dramatically better.
It's about demanding that internal accountability.
That self -correction makes the output cleaner,
for sure. But the builder mindset also means
connecting the AI outside, right? How do we take
that better text output and turn it into finished
products? Ah, yeah. This is where tool integration
comes in. We're moving beyond just text. You
can connect third -party apps now, things like
Canva or Figma, right there in the ChatGPT settings.
OK, so think about presentations. Let's say you
upload a five -page document, maybe a quarterly
marketing report. Pretty dense. You could tell
ChatGPT, I want a 10 -slide draft, maybe five
slides summarizing findings, three on key takeaways,
and I don't know, two on next steps. Right, so
chat GPT reads the content, figures out the summaries,
and then tells the connected Canva tool, hey,
generate some professional designs for this.
You instantly get like a 90 % finished presentation.
That's a huge time saver. You're not starting
from scratch. You're basically editing something
that's almost done. Or take Figma. If you need
a workflow diagram, maybe for onboarding a new
employee, which can get complicated, you just
give it simple text instructions, define your
colors, say blue boxes for actions, orange diamonds
for decisions, and boom, it generates a perfect,
shareable flowchart. The real power here is that
it removes the technical skill barrier. You don't
need to be a Canva wizard or know how to manually
draw stuff in Figma. You just describe the result
you want, and the AI builds it. So if the core
difference is forcing that self -reflection and
integration, what's the single biggest takeaway
for everyday prompt writing? Always instruct
the AI how to think before you tell it what to
write. Okay, the next big shift then. This involves
giving the AI a persistent brand persona. Basically
getting rid of that repetitive work of explaining
who you are and what your style is every single
time you start a new chat. Yeah, that constant
explaining leads to what we call prompt fatigue.
It's tiring. And that's exactly why the custom
instructions feature is, well, it's critical.
It acts like ChatGPT's long -term memory about
you and what you like. We saw this great example
using a small local business. Let's call it...
Happy Bakery. You set up the instructions to
define the brand as warm, friendly, and you enforce
specific rules like use short, simple sentences,
maybe use certain emojis like a cupcake carry,
and always ask customers to visit the actual
bakery. And now every single new chat, whether
you ask for an email, an Instagram post, whatever,
it automatically adopts that voice. Those rules,
the systemic benefit is just huge. You save so
much mental energy because you're not constantly
reminding it, hey, be friendly or don't use complicated
words. But sometimes you don't actually know
your own brand voice that clearly right. You
just kind of write what feels right at the time.
Which leads to this really interesting strategy.
Let the AI figure out your brand guide for you
first. Exactly. You upload, say, five to ten
pieces of your own content that you really like.
Maybe blog posts, newsletters, emails you've
written. Then you ask ChatGPT to analyze them.
Act like a brand consultant for me. And it creates
this detailed brand voice guide. It looks at
your existing tone. Is it motivating? Gentle,
your sentence style short and punchy, long and
detailed, even your brand personality like. Do
you sound more like a tough coach or a helpful
friend? You know, I still wrestle with prompt
drift myself sometimes. Honestly, I'll be like
15 minutes into writing something with it. And
I realized the tone has gone completely off track,
maybe too corporate or something. And then I
have to stop. backtrack, reset the context. Having
the AI define my preferred style upfront saves
me from that constant resetting. It's a major
time drain otherwise. And once you've got that
memory institutionalized, you can actually organize
multiple specific personalities. The sources
talk about using the memory feature to save these
personas linked to a trigger word. Yeah, this
is pretty cool. You could create, say, the tough
editor persona, train it to be super direct,
really strict, just cut out every unnecessary
word, and you trigger it just by typing edit
now. Or maybe you need a brainstorm buddy sometimes,
right? Triggered by crazy ideas. It just instantly
shifts its whole role and tone based on that
one word. It's powerful. So by institutionalizing
our style, these personalities... Are we maybe
risking creative stagnation or is it just about
increasing speed? Institutionalizing style frees
up your mental energy for the higher level creative
thinking. All right, let's shift gears a bit.
Let's talk about complexity. The next set of
strategies really focuses on using AI to find
patterns, whether that's in like raw data or
generating creative stuff without needing technical
skills. Yeah, let's start with data storytelling.
The problem is pretty common. You get these big
Excel files full of numbers, right? sales figures,
whatever. And it's hard to see the trends hidden
in all those rows and columns. So the solution
is you upload the raw file like a .xlsx or .csv
- and you ask the AI to find the story in the
numbers. You give it a specific prompt. Tell
me the best and worst months. What's the general
trend? Summarize the key takeaways. Maybe four
bullet points. And crucially, ask for one suggested
action based on that data. It turns confusing
numbers, like sales reports or even big bank
statements, into simple, actionable insights
you can use right away. It's kind of like having
an analyst on call instantly finding patterns
your own eyes might just skim over. Okay, now
shifting to creation. This is where things get
really interesting, I think. Professional video.
We know AI video tools are coming. They're getting
better fast. But the bottleneck is often the
prompt quality. So Chet, GPT's role here is writing
that perfect, super detailed cinematic prompt.
Right. Take a simple idea like a happy dog running
on a beach. This is not going to give you a great
video, it's too basic. So you ask ChatGPT to
rewrite it cinematically. Force it to add details.
Instruct it. What breed of dog? What specific
time of day? What camera angle should we use?
What precise feeling should the scene evoke?
And the result is a prompt that specifies, like,
a golden retriever joyfully running along the
wet sand precisely at sunset, camera flying low
beside the dog, capturing the glint of the golden
light on its fur. The overall feeling must be
warmth and freedom. That's a prompt that an AI
video tool can actually work with. Whoa, yeah,
imagine scaling that. A million unique high -quality
video scenes generated every single day, all
from just text instructions. Yeah. I mean, that
really is the future of digital content, isn't
it? And finally, for maybe folks with websites
that feel a bit static or boring, our sources
point into building simple website widgets using
ChatGPT. You don't need to hire a programmer
for everything. You can ask ChatGPT to write
the full code, HTML, CSS, JavaScript, for something
like a simple interest calculator. or maybe a
calorie counter. And it generates the code. Usually
pretty well. You can just copy that code block
and paste it directly into your website builder
like WordPress or Wix or whatever you use. It
adds useful interactive features to your site
in minutes. That's a massive return on your time
investment. So which do you think is the bigger
time saver overall? using AI to analyze existing
data or using it to generate totally new creative
assets. Analysis reveals where you need to act.
Creation implements how to act quickly. Okay,
our final core shift. This one feels maybe the
most personal. It's about transforming the AI
into more of a thinking partner, even a decision
maker alongside you. Yeah, and this can start
with active learning. Say you upload a complex
document, maybe a report on Vietnam's economic
history. Don't just ask for a summary. That's
passive. Instead, ask ChatGPT, act like a demanding
teacher, tell it. Quiz me one question at a time,
hold me accountable, explain why my answers are
right or wrong, and keep going until you hit
a certain score. That's active learning. It really
helps with retaining the information much more
than just reading a summary. Okay, next up, organizing
big decisions. And when you're planning something
complex, maybe comparing Joe A versus Joe B,
or deciding between Da Nang and Sapa for a vacation,
that single chat thread can get really messy,
right? Pros and cons all mixed up. The power
move here is using the branch chat feature, which
basically just means duplicating the current
chat thread. So you explore option A completely
separately from option B to distinct, clean conversations.
It keeps your thinking organized and stops that
feeling of cognitive overload. And we shouldn't
overlook practicing soft skills. Using the mobile
app's voice mode lets you practice difficult
conversations out loud. Ask it to role play,
like be a professional, serious HR manager and
ask me five tough interview questions. Yeah,
saying the words out loud actually builds muscle
memory. You get to practice your phrasing, work
through the awkward pauses, fix the stammers
in private before you face the real high stakes
conversation. It really helps. And all this kind
of culminates in the ability to build a custom
GPT advisor. This is like a specialized version
of the AI that you train to think and talk like
a specific persona or maybe a certain philosophy.
Right, like the example of building a stoic advisor
GPT. You train it specifically on stoic principles,
instructing it to focus on what's controllable,
on wisdom, on calm advice. So when you ask it
about, say, a conflict at work, it won't just
give you standard HR advice. It might say something
like, you cannot control your co -workers' actions,
only your reaction. Focus on what is within your
sphere of influence. It gives you this entirely
different specialized lens to look at your problems.
It really becomes a targeted thinking partner.
So does building a specialized GPT like that
for advice fundamentally change how we relate
to AI? Does it move from just a tool to more
of a collaborator? Yes, I think it does. It evolves
the tool into a specialized, highly targeted
thinking partner. So the common thread here...
Across all of these methods, it seems pretty
simple, really. Top users don't just use ChatGBT
for one -off answers. They build systems. They
teach the AI about their brand, their voice,
their processes, their goals. It's about persistence.
Exactly. The real shift is moving from that sort
of transactional input output to a more persistent
contextual collaboration. The difference between
a regular user and that top 1%, it isn't really
about knowing more hidden features. It's about
using the AI as a partner. a partner that holds
long -term context and critically self -corrects
its own output before you even see it. So maybe
start small. Pick just one method that feels
like it fits your workflow right now. Maybe setting
up a simple custom instruction for your brand
voice. That could be a good first step. The more
you teach it about what matters to you, the more
valuable it's going to become. Now here's something
to think about. If you used this system building
approach to automate one really major, painful
task in your week, what system would you build
first?