The difference between a generic AI answer and
a really excellent one. It's all context, 100%.
Yeah, it boils down entirely to that. We often
treat these incredible models, like GPT 5 .2,
as if they're just... The fancy Google search
bar. Exactly. We ask these simple questions,
and then we're surprised when the output is,
you know, boring. It's flat. It's useless, frankly.
But they aren't calculators. They're thinking
machines. And if you want high -level thinking,
you have to give them a high -level job to do.
A role, a process, specific rules. And that's
the foundation of what we're looking at today,
this mega prompt framework. Welcome to the Deep
Dive. Today we've synthesized research on 10
very specific pretty advanced prompting frameworks.
And these are designed to save you hours of work
every week. We're talking across research, creative
stuff, design, even your personal finances. Our
mission today is to show you how to start treating
GPT -5 .2 less like a tool and more like a true
thinking partner. So let's get into it. The most
challenging area first. Deep research and strategy.
Okay, let's unpack it. The biggest pain point
with high stakes research isn't just getting
an answer, right? It's that thing everyone worries
about AI hallucination. Where the model just
confidently makes things up. Yeah, and you can't
trust the output unless you force the model to...
Self -correct to check its own work. The fix
here isn't just asking a better question. It's
demanding a whole process. Exactly. We introduced
the chain of thought method. It's actually pretty
simple in concept. You're just telling the AI
to show its work. To think step by step before
it gives you the final answer. And this first
mega prompt, it turns the AI into a deep dive
analyst. You give a very specific role. Like
what? A senior market intelligence officer, 15
years of experience, specializing in emerging
tech. Then you tell it how to think. First, it
has to fact check everything. And this is critical.
Ignore any data older than 12 months. Keeps the
analysis fresh. Right. No outdated conclusions?
Here's where it gets really powerful. You force
a multi -perspective simulation. The AI has to
run an internal debate. A debate between who?
Between, say, three different experts. An optimistic
investor who's all about growth, a skeptical
regulator focused on risk, and a pragmatic consumer
who only cares about price and usability. Whoa.
I mean, just imagine the efficiency there. Simulating
a debate between three high -level experts instantly
just to find the key risk. That scale is... that's
powerful. And the final output has to zero in
on one thing. The contrarian view. the unpopular
opinion or the risk that everyone else is missing.
So why is forcing it to find that contrarian
view the most valuable part? It reveals risks
your competitors haven't seen yet. Okay, so if
deep research is about verification, our next
area, writing, is all about trust. And attention.
Prompt two is for structuring technical papers
that people, you know, decision -makers will
actually read. The big challenge there is that
technical writing is often so dry, especially
for a busy executive. So the solution here is
to use a psychological flow. You're structuring
the document to guide the reader on an emotional
journey, really. You start with the hook, a specific
costly problem, the pain. Then you show them
the gap. which is why current solutions are failing.
You introduce your idea as the logical next step.
Then you provide the proof, the technical stuff,
and you end with the payoff. A clear ROI. It's
a full story. And there's a style rule here that's
really important. Grade 10 readability. Yeah.
We know busy executives prefer things that are
easy to understand. They don't want jargon. It's
not about sounding smart. It's about being clear.
And you have to ban certain words. Critically.
You have to ban the AI filler words. Things like
unleash, game changer, revolutionary. How does
banning those typical AI filler words make the
writing stronger? It forces the model to find
better, more specific terms. Top three moves
us from analysis into, I guess, active strategy,
using the AI to basically play a war game against
your own product. You assign it the role of a
ruthlessly efficient competitive strategy consultant,
someone who is paid to find your flaws. And the
task is a pre -mortem SWAT analysis. A pre -mortem.
Exactly. You tell the deep dive into things like
your pricing psychology, your feature gaps, with
brutal honesty, and what customers really think.
The stuff they hate but don't tell you. And the
prompt ends with this, this power question. If
you were their CEO, exactly how would you attack
us to steal our market share? It forces the AI
to find your weakest spots. to give you the kill
strategy against yourself. So why is knowing
your weakness before you launch so vital? It
lets you fix fatal flaws before they become fatal.
Okay, let's shift gears, part two. Creative content
creation. The focus here moves from data to pure
attention. For viral social media posts, it is
all about the hook. The first line is like 80
% of its success. So the role you assign is a
viral social media strategist. And the formula
for viral... requires that the hook is a pattern
interrupt, something that stops the scroll, a
controversial statement, a surprising fact, a
direct question. And the body has to be skimmable,
short sentences, bullet points, and a twist in
the middle. Counterintuitive insight, yeah. Yeah.
And the constraint you add is asking for three
different hook options for A -B testing, plus
a visual idea for each, a meme, a chart, whatever,
saves so much time. So beyond that hook, what
does asking for the engagement bait do? It forces
people to comment by ending with a specific question.
Prompt five tackles what I think is the biggest
tell for AI writing. That robot smell. The flatness,
yeah. It sounds clinical because the sentence
lengths are all the same. The solution is to
explicitly fix something called burstiness. Which
is just the variation in sentence length. You
have to tell the model to mix very short, punchy
sentences with longer, more complex ones. It
creates a human rhythm. And again, we're banning
words. The AI -ism. Oh, this is a big one. You
have to get rid of words like realm, landscape,
delve, tapestry, fostering. There did giveaways
of a generic answer. I have to admit, I still
wrestle with prompt drift myself, especially
when the AI defaults to those words like delve
or tapestry. Banning them is brilliant. And you
double down on show, don't tell. Don't let it
say it was efficient. Force it to say it saved
us four hours. So how does requesting that high
burstiness help the content pass AI detection
tools? It breaks the model's uniform safety rules,
making the rhythm more human. And for the sixth
prompt, structuring a persuasive presentation,
the goal is to move beyond bullet points and
create an actual story. So you assign the AI
the role of a presentation coach. For TED speakers
and VCs, you tell it to use the hero's journey
narrative. Status quo, challenge, resolution.
But the detail here is about the emotional goal
for every single slide. Yeah, this is the key.
You say, for slide five, I want the audience
to feel fear. For slide eight, hope. For slide
12, trust. Why is it so vital to define the emotional
goal for every single slide? Because people buy
and invest based on feeling, not just cold, hard
numbers. Welcome back to The Deep Dive. We're
moving into our final section now, looking at
design, learning, and some critical financial
analysis. Prompt 7 is all about designing beautiful
and, more importantly, functional UI UX. You're
not just asking for a layout. You're telling
the AI to act as a senior product designer and
to focus on the whole user journey and the edge
cases. Right, the edge cases. The critical deliverable
you ask for is the interaction stays. What happens
on hover, active disabled, and especially on
error? This prevents so much bad design. It forces
the AI to plan for messy situations like slow
internet or when a user makes a mistake. And
that gives you a complete design framework, usually
with the code ready to go. So why is planning
for those edge cases the real difference maker?
A bad design forgets what the button looks like
when the user messes up. Promptate is about personalized
learning. I think we all know online courses
can be way too long. Packed with theory you don't
need. So here we apply the Pareto principle,
the 80 -20 rule. The AI becomes an expert curriculum
designer. The goal is to find the critical 20
% of concepts that get you 80 % of the results.
So if you're learning Python for finance, it
just cuts out all the abstract computer science
and focuses right on the libraries you'll actually
use. And the output has to include specific topics,
links to free resources, and one micro project
each week. and a list of common pitfalls, the
beginner mistakes. How does including those pitfalls
help the learner save time? It helps you avoid
time -wasting routes and useless theory. OK,
prompt nine, analyzing stocks and investments.
And we have to start in the disclaimer here.
A big one. AI is a research assistant, not a
financial advisor. Always check the facts yourself.
So we give it the role of a financial analyst,
but one with a very conservative, risk -averse
mindset. And the framework focuses on two things.
The business model, is it recurring revenue or
one -off sales? And the moat, what protects them
from competitors. But the crucial part, the mandatory
part, is the bear case. Absolutely. The AI has
to list five specific reasons the investment
could fail. It forces critical, defensive thinking.
And you make it compare valuation metrics against
the top three competitors. So why is forcing
the AI to act like a critic, the bear case, so
good for decision making? It combats our bias
to only see the good side of a stock we like.
And finally, prompt 10. Managing taxes and deductions.
This has huge immediate value. Especially for
freelancers or small business owners, yeah. The
AI becomes a tax strategist for your specific
country and state. The high value output is called
the deduction hunter. Which lists every possible
write -off you might be eligible for. Home office,
electricity, software subscriptions, everything.
It also gives you a list of red flags, what might
trigger an audit in your industry, and a checklist
of all the documents you'll need. So which specific
output from this prompt gives the most immediate
value for a freelancer? The deduction hunter.
It's a list of write -offs to discuss with their
accountant. So if we zoom out here and look at
the big idea connecting all 10 of these, what
is it? It's that GPT 5 .2 is, well, it's pretty
useless if you don't control it. The power is
in the frameworks. It's not about just copy pasting.
It's about understanding the thinking process
you're creating. Exactly. The difference is moving
from a simple question and answer to providing
context, the role, and forcing self -correction
with things like chain of thought or the bear
case. The path to becoming an expert user isn't
about some secret command. It's learning how
to tell the AI how to think, not just what to
say. The source material we looked at recommends
choosing just one big problem you have right
now and just trying one of these mega prompts
on it. And here's a final thought to Chuan. We've
seen that every really effective prompt forces
the AI to find the opposite view, the contrarian,
the bear case, the kill strategy. What if we
applied that same chain of thought framework
not to an AI, but to our own most stubborn personal
biases? Could forcing ourselves to simulate the
skeptical regulator or the bear case against
our own deeply held beliefs, could that be the
ultimate prompt for personal growth? Some to
chew on. Go try one of these frameworks, see
what a difference it makes. And thank you for
joining us for this deep dive.