Initial word on the street, ChatGPT -5 sucked.
It felt like a downgrade, yeah. A step backward
for many. But here's the thing, maybe, maybe
that's only if you were using it completely wrong.
Today we're going to unpack the hidden one thing
that really changes everything. Welcome to the
Deep Dive. We're diving into a topic that genuinely
caught us and many others by surprise, ChatGPT
-5. The internet was just flooded with disappointment.
Yeah. And honestly, I was right there with them
at first. I really was. It felt like my shiny
new tool just wasn't, you know, performing. But
what we quickly realized is that it's not the
model that's the problem at all. We were just
driving this like highly advanced spaceship like
it was our old family sedan, you know. So this
deep dive, it's all about getting you into the
pilot seat. Okay. So our mission today is clear.
We want to show you how to unlock ChatGPT -5's
mind -bending potential. We'll look at four revolutionary
prompting methods and even reveal a secret cheat
code from OpenAI itself. We're basically going
to try and transform how you interact with AI.
So let's start with that initial misunderstanding
then. What was the common complaint about ChatGPT
-5? And why do you think it became so widespread
so quickly? Well, the core issue was simply a
matter of perception, wasn't it? Just how people
were looking at it. Most people approached it
expecting, you know, just another incremental
upgrade, like GPT -4 .5 or something. But it's
fundamentally a different system, completely
different architecture. Think of the old models
like a garage full of specialized cars, right?
You had your race car for speed, maybe a Jeep
for deep, complex thinking. Maybe the Prius for
those simple, really energy -efficient tasks.
Chat GPT -5, it's different. It's a router model.
A router model. Explain that a bit. Yeah. So
imagine like a central traffic controller, but
for its own brain. It's an AI that intelligently
directs your request to its most suitable internal
component or engine. It decides, OK, does this
need brainstorming? Does it need research, maybe
code generation? All within the same interaction.
It's an all in one system with these different
capabilities built right in. OK, so if we've
got this incredible all in one spaceship, as
you put it. Where did everyone go wrong when
they first tried to fly? What was the mistake?
The crucial part, the bit everyone missed, is
that it's now largely manual. The AI doesn't
automatically pick the right settings for you
anymore. Not like before. You, the user, you
have to be the pilot. You're controlling every
variable. And that's why the results were so
disappointing for so many people initially. It
was like expecting an automatic car, but being
given, you know, a stick shift with zero instructions.
So the core shift for us, the users, is really
stepping up and becoming the pilot. Taking control.
Exactly. We're now pilots, manually controlling
its complex internal functions. Okay, let's get
right into the cockpit then. The first two critical
controls on this new spaceship's dashboard are
what you've called the gearbox. That's reasoning
level and verbosity. Right, exactly. Think of
reasoning level as the horsepower, how much thinking
power you want. The problem was, without explicitly
telling it otherwise, chat GPT -5 just defaults
to its simplest, most energy efficient and just
coasting, which is great for quick surface level
stuff, maybe, but not much else. And this resulted
in what's often called AI slop. You know, those
generic, bland, kind of useless responses we
all got tired of. Yeah. The solution is actually
pretty simple. You explicitly tell it how hard
to think. Think about this. It's kind of like
first gear. Basic. Think harder about this. Shifts
you up to third gear. More effort. And then ultra
thing about this. That's fifth gear. Engaging
maximum cognitive power. So the AI can actually
think harder if you tell it to. But how dramatically
does that impact a real world task? Give us an
example of something that really showed you the
difference. OK, yeah. We saw this with a Discord
community blueprint example. It was pretty striking.
A simple prompt like design a Discord community
for AI entrepreneurs. Just that. It gives you
generic channels, you know, hashtag general,
hashtag random. Stuff you'd expect. Pretty standard.
Very basic. But then you add, you must think
harder about this. Just that phrase. And the
AI absolutely transforms. It's like a switch
flips. It becomes this world -class consultant.
Seriously. It delivers a professional blueprint,
detailed channel structure, role hierarchy, custom
emojis, even a whole engagement strategy. Whoa.
I mean, imagining the AI actually shifting gears
internally, going from just brainstorming to
acting like a top tier consultant. That's just
incredible to see. Okay. That's reasoning and
verbosity level. Is that like controlling the
fuel flow? How much output we actually get? Exactly.
Yep. It gives you reliable control over the output
depth, how much detail you want. We found three
main levels seem to work well. Low, think too
long, didn't read, like a one -two sentence summary.
Medium gives you more of an executive summary,
the key details. Or high, which is like a deep
dive for really comprehensive context. So, for
instance, asking for a breathing improvement
plan but specifying high verbosity turns basic
tips into a complete professional wellness program.
It might even include sources and detailed action
plans. Interesting. Now, I've heard you mention
something called a debate club prompt. That sounds
intense. How does forcing the AI to argue with
itself actually elevate its output? Oh, it's
fantastic. It's a pro -level upgrade, really,
because you're essentially making the AI self
-critique before it gives you anything. You force
it to adopt like four distinct personas internally.
There's an aggressive red team critic finding
flaws, a supportive blue team champion highlighting
strengths, then the customer persona focusing
on user needs, and finally the CEO making the
call. So it's not just generating an answer,
it's guaranteeing a deep multi -perspective analysis
internally first. It considers all the angles,
especially the user's needs and potential problems,
before finalizing the output. So it really forces
the AI to look at the problem from every angle,
especially thinking about the end user. Yes,
ensuring a truly comprehensive, multi -perspective
analysis before it delivers. Okay, so we've tuned
our engine with reasoning. Control the output
depth with verbosity. But what if we need more
than just one function at a time? That seems
to be where this next method comes in, the one
that makes ChatGPT -5 feel really next gen. You
call it the utility -built approach, using the
AI as a multi -talented agent. And just for clarity,
tool calling here means the AI picking and using
its own internal tools or external connections
to get a job done right. Precisely. Exactly right.
Standard AI, it's often like a superhero with
just one power, you know, super. speed, or flight.
ChatGPT -5 is more like Batman. It's got the
full utility belt. It has these incredible built
-in tools, image generation, PDF creation, web
research, code generation, and more, lots more.
The real secret, the power move here, is commanding
it to use multiple of these tools simultaneously,
all in a single complex request. Okay, give us
a mission briefing example. What kind of multifunction
request can it actually handle in one go? Right,
imagine this. You prompt it, act as my full -service
creative agency. Just start there. And then in
that single prompt, you ask it to, one, come
up with a logo concept. Two, create a one -page
brand guideline PDF based on that. Three, draft
a community announcement tweet about the new
brand. And four, research the top three competitors
in your specific niche all at once from one instruction.
And the after -action report, how did it actually
perform on that complex mission? Honestly. It
was incredible, genuinely stunning. In just one
minute and three seconds, yeah, 63 seconds, it
completed all four distinct tasks. It generated
professional -looking logo concepts. It built
a comprehensive brand guideline PDF. It drafted
an optimized tweet ready to post. And it delivered
a detailed competitive analysis with live links
to the sources. We looked at its internal log
afterwards. It showed it consulted 23 different
sources and made four separate tool calls, image
gen, PDF, web search, all orchestrated perfectly.
Totally autonomously. Wow. So it's essentially
like commanding a whole team, a full creative
agency, just sitting there waiting inside the
AI. Exactly. A full creative agency, all operating
from one comprehensive prompt. Okay, moving on.
This next method, it's about overriding that
default AI tendency to be a bit of a people pleaser,
which, let's be honest, often leads to just good
enough results, not great ones. You call this
activating the AI's internal red team. Yeah,
exactly. A red team. In, say, cybersecurity or
military strategy, its job is to attack a system
to find all the weaknesses, right? This prompt
basically forces the AI to red team its own work
before showing it to you. It's based on an actual
open AI template they use internally, apparently.
The AI privately designs a quality rubric for
the task. Then it iterates internally, critically
assessing its own drafts against that rubric
until it scores highly across all categories.
It simply won't deliver the output until it deems
the work world class. It fundamentally shifts
the AI's objective from just answering the question
to creating a genuinely high quality product.
How does this play out in practice, like say
for game development? Oh, that was a great example.
For a 3D game development task, a simple standard
prompt produced, frankly, a pretty basic kind
of boring game concept. Serviceable, maybe. But
then using the red team prompt structure, the
AI delivered something far more advanced, much
more polished. It included features we didn't
even ask for, like a cool slow motion mechanic,
complex enemy models, sound effect suggestions,
even advanced physics implementations. It's like
it holds itself to a much, much higher standard
internally. I have to admit, I still wrestle
with prompt drift myself sometimes. Yeah. You
know, where the AI starts out fine, but slowly
steers away from your original intent over a
long conversation. Yeah. So the idea of having
the AI almost coach itself to stay on track or
even prove its own quality, that's kind of amazing.
You also mentioned a Socratic self -correction
upgrade. What's that add? Yes, this takes the
self -critique idea even further. It's more structured.
A Socratic self -correction prompt forces the
AI into this really structured internal dialogue.
First, it acts as a senior critic to identify
flaws in its own plan or code. Then it switches
hats, becomes the original developer to justify
its choices, explain the reasoning. Then it goes
back to being the senior critic to suggest concrete
improvements. And finally, as the original developer
again, it refactors the code or plan based on
that critique. It's a really rigorous internal.
peer review process happening inside the AI.
That sounds incredibly robust. It really seems
like it pushes the AI to self -improve significantly.
What's the biggest benefit you see from that?
It dramatically elevates the AI's internal quality
bar, delivering truly exceptional polished outputs
consistently. Okay, this next technique is a
bit different. It's more meta. It promises to
accelerate our own learning curve. It's about
having the AI analyze and improve our own prompts,
like having a personal prompt engineering coach
built right in. Precisely. That's exactly it.
The AI understands its own internal architecture,
its biases, its capabilities way better than
any human ever could, right? So it's the ultimate
expert on how to talk to itself effectively.
The metaprompt structure is pretty straightforward.
You give the AI three crucial things. One, your
desired outcome, what you wanted it to do. Two,
the flawed or disappointing behavior it actually
produced. And three, a clear constraint or request
for how it should improve your prompt. How did
this work with that 3D game example you mentioned
earlier? Right. So that initial prompt just asked
for engaging gameplay, which, as we've discussed,
is way too vague. Doesn't give the AI enough
direction. So using the meta prompt, we fed that
back to the AI. We said the gameplay wasn't engaging
enough. How should we change the prompt? And
the AI literally responded with specific edits
to our original prompt. It suggested things like,
you should explicitly ask for three different
enemy archetypes with distinct behaviors, and
you need to specify two particular collectible
power -ups. It's the AI directly teaching the
human user how to be a better prompter to get
better results from it. It's amazing. So meta
-prompting is essentially like getting into a
rapid feedback loop to upgrade my own prompting
skills, guided by the AI itself. Yes, exactly.
It's a continuous feedback loop that accelerates
your prompt engineering mastery. Okay, this next
one sounds intriguing. It's described as the
ultimate cheat code, a powerful tool straight
from OpenAI's own engineers. But you say it's
hidden. Not in the main chat GPT interface we
all use. What is it? That's right. It's kind
of tucked away. It's called the prompt optimizer,
and you'll find it over on OpenAI's developer
platform, in the playground usually. You feed
it your prompt, maybe one that's good but not
perfect yet, and the optimizer analyzes your
prompt's underlying intent, then it actually
rewrites it for you. It adds things like technical
specificity, much clearer structural instructions,
maybe enhanced constraints, all designed to get
the absolute maximum performance out of GPT -5.
It's like having an OpenAI master prompt engineer
sitting next to you, refining your prompts every
single time. You mentioned a real -world transformation
using this optimizer in our 3D game prompt example.
What did it actually do to the prompt? How did
it change it? yeah so the original plumped after
some basic refinement was decent you know it
was okay but the optimized version that the tool
produced it was a different beast entirely it
added much more specific technical requirements
things we hadn't even thought to include clearer
more evocative aesthetic guidelines for the art
style Things like specifying the precise output
format for the code, the target frames per second,
very specific enemy archetypes and power -up
mechanics, even the desired internal file structure
for the game assets. The difference in the prompt
itself might look subtle at first glance, but
the impact on the final output, it was massive.
It yielded a significantly better, more complete
final product. That sounds incredibly powerful
for getting top tier results. Is this optimizer
truly a game changer for consistently high quality
outputs? Absolutely. It's like having an open
AI master engineer refining your prompts every
time. OK, so we've explored these individual
really powerful techniques, the gearbox, the
utility belt, the red team, meta prompting, the
optimizer. Now you're saying it's time to assemble
them all together, like forming the unstoppable
super robot Voltron. Yeah, exactly like Voltron.
A Voltron prompt strategically combines all these
methods into one master prompt. It includes a
really clear objective, specific reasoning and
verbosity controls, instructions for multi -tool
utilization, built -in self -reflection parameters
like the red team, and maybe even an optimization
pass using that cheat code. It's basically a
master prompt architecture designed for complex,
multifaceted tasks that need high quality across
the board. And you used a Voltron prompt for
something called an... Agency in a Box mission.
Was a goal there and what was the outcome? Right.
The goal was ambitious. Have the AI act as a
full service creative agency to build an entire
brand identity system for a hypothetical online
coding community. Logo, guidelines, messaging,
the works, all from a single comprehensive Voltron
prompt. The result, it delivered a complete professional
grade brand identity system. Seriously, the kind
of project that would normally cost a company
well into six figures and take weeks, maybe months.
And the AI delivered it in a matter of minutes.
staggering. And beyond even that massive single
prompt, you mentioned something called an agentic
chain. What's that? Taking it even further. Yes,
exactly. Instead of trying to cram absolutely
everything into one massive prompt, you can chain
multiple specialized Voltron prompts together.
Think of them as different AI agents working
in sequence. So agent one, maybe the strategist,
does the initial research and planning using
a Voltron prompt focused on that. Agent two,
the designer, takes that strategic context and
uses its Voltron prompt to generate visual concepts
and brand assets. Then agent three, the copywriter.
it's the strategy and the visuals, and uses its
prompt to create all the marketing materials
and website copy. It's like having a coordinated
team of world -class experts, each building on
the last one's work, all managed seamlessly through
these chained prompts. So the Voltron prompt,
especially when chained, really takes the AI
from just being a tool to acting like a full
-blown coordinated project team. It truly acts
as a comprehensive multi -agent system for complex
projects. Reflecting on all this power, if ChatGPT
-5 is actually so capable when used correctly,
why was there so much initial disappointment?
What was this great misunderstanding really all
about? Well, fundamentally, it wasn't a failure
of the model itself. Not at all. The tech was
there. It was honestly a catastrophic failure
of communication and user onboarding from open
AI. That's the hard truth. Imagine selling someone
a Formula One race car, but marketing it as just
a simple family sedan and then hiding the gear
shifter and the instruction manual. They buried
these essential prompting techniques deep in
documentation. If anywhere, they offered basically
no. tutorials for these advanced features. They
kept the cheat code optimizer hidden away on
the developer platform, and they largely ignored
the confusion and frustration from existing users
trying to figure it out. It left a lot of people
feeling confused and, frankly, abandoned. And
this naturally leads to what you called an unfair
advantage for those who do figure out or learn
these techniques, right? Absolutely. Right now,
it definitely does. Most users are kind of fumbling
around, like someone who just bought a simple
store -bought magic kit, basic tricks. Power
users, the ones mastering these methods we've
discussed, they become like professional magicians.
They're doing stage illusions. They unlock these
incredible superpowers. Truly superior code generation,
where the AI acts almost like a co -creator.
Comprehensive research capabilities that turn
it into an AI intelligence analyst. Professional
level content creation, like having an AI creative
agency on tap. And the ability to tackle really
complex problems using the AI as a strategist.
So learning these methods... Adjusting that time,
it truly gives us a kind of AI superpower compared
to the average user. Yes, absolutely. It's architecting
a process for consistently exceptional, high
-quality AI output. So wrapping this up, what
does this all mean for you, the listener, trying
to navigate this? ChatGPT -5 is not a failed
upgrade. Far from it, actually. It seems it's
a revolutionary system. Its initial perceived
complexity, that steep learning curve. It isn't
really a bug. It sounds like it's genuinely a
feature designed for more control. Yeah, think
of it again like that manual transmission race
car we talked about. To a complete beginner,
yeah, it might feel broken, clunky, frustrating
to drive. You'll stall it a lot. But to a skilled
driver who learns the clutch and gears, it offers
unparalleled power, incredible speed, and precise
control you just can't get from an automatic.
Mastering these new manual controls, the reasoning
levels, the verbosity, the tool calling, the
self -reflection prompts, it definitely takes
an investment of your time and effort. It's not
instant. But the results you can get. They are
genuinely, truly mind -blowing when you get it
right. So the choice is really yours, isn't it?
You can continue getting those generic, maybe
adequate AI responses. Or you can invest the
time to learn these techniques and unlock ChatGPT
-5's really extraordinary potential. Your AI
superpowers are there, waiting. You just have
to learn the right way to ask for them, the right
way to pilot the machine. And, you know, this
raises a really important, kind of exciting question
looking forward. What other hidden features or
maybe manual controls might exist right now or
be coming soon in this rapidly evolving world
of AI? Features just waiting for curious minds
like yours to discover them, to leverage them
for completely unprecedented creativity and problem
solving. What will you build next now that you
have these tools, these new ways of thinking
about interacting with AI? Outro music.