So it's January 2026, and we're sitting on top
of, you know, the most powerful computational
engine in human history, GPT -5. The benchmarks
are just, they're off the charts. They're absurd.
And the reasoning, I mean, theoretically, it
rivals human experts in almost every field. And
yet, I was talking to a friend yesterday, a really
smart guy, uses AI for coding, and he said something
that just stuck with me. He said, I feel like
I'm fighting it. He types a question and the
answer he gets back feels. It feels lazy. It
feels bored. It kind of feels cheap. Yeah. And
it's this strange paradox where the model is
smarter than ever, but the user experience feels
like it's degrading. It's the GPT -5 paradox.
You have this Ferrari engine, but the car is
locked in first gear. And you're pressing the
gas, but it just won't go. It just won't go.
So today, we're going to figure out how to shift
gears. We are deep diving into a guide called
Five Advanced Chat GPT Tricks for GPT -5 Mastery.
Right. But after reading through this, tricks...
That feels like the wrong word. It's more like
we're decoding a new psychological relationship
between us and the machine. I think so, too.
Because it turns out there's an invisible decision
maker standing between us and all that intelligence.
Right. The router. And if you don't get the router,
you're essentially getting the discount version
of the AI, no matter how much you're paying for
that pro subscription. Welcome to the deep dive.
Today, we're going to break down this idea of
router influence. We'll look at the architecture
of 2026, and then we'll walk through five really
specific strategies. Things like trigger words,
radical specificity. And something called self
-reflection loops, which supposedly force this
router to actually give us the intelligence we're
asking for. What I love about this whole analysis
is that it moves us away from prompt engineering
as some kind of mystical art. Yeah. And it treats
it more like system administration. It's just
about understanding that there's a gatekeeper
and you need the password. Let's linger on that
gatekeeper for a second, the underlying architecture,
because I think a lot of us still have a mental
model from, you know, 2023 or 2024. Oh, for sure.
So walk me back to the vintage era of AI. How
did we used to interact with these things? Well,
it was manual transmission. Yeah. Think back
to ChatGPT in late 2024. Yeah. You had that little
drop down menu at the top left. Right. You'd
log in and you had to make a conscious executive
decision. I am writing a poem, so I will select.
GPT -40 or... I'm solving a complex physics problem,
so I'll need a one preview. Exactly. You, the
human, you were the load balancer. You decided
how much horsepower to use. And there was a tangible
difference. If I pick the big reasoning model,
I knew I was going to stare at a spinning circle
for 30 seconds. Yeah, you waited. But I knew
I was thinking. I was basically buying depth
with my time. Precisely. But here's the reality
of 2026. Open AI. and really all the labs, they
realized that humans are, well, terrible at load
balancing. We're wasteful. We are so wasteful.
We would use these massive energy -lucking models
to ask for, like, a chocolate chip cookie recipe.
Right. And that burns a tremendous amount of
compute and money for a task a pocket calculator
could almost do. So they took the keys away from
us. They automated the transmission. Now, under
the hood, there are basically three engines,
base, thinking, and pro. But you don't see them.
Okay. When you hit enter, your prompt goes to
the router. This is lightweight, invisible AI
layer that just acts as a triage nurse. A triage
nurse. I like that. Yeah. It scans your request
in milliseconds and decides three things. Which
model gets the task, how much reasoning budget
to unlock, and how verbose the answer should
be. So it's a cost -saving mechanism. It is aggressively
optimized for efficiency. And that's where all
the friction comes from. If your prompt is vague
or short or just looks simple, the router defaults
to the base engine. It's cheap, it's fast, and
it saves the data center money. This explains
the laziness. So if I ask for a business plan,
but I ask it really casually like, hey, write
up a plan for a coffee shop. Right. The router
sees a short sentence, it classifies it as low
complexity, and just gives me the fast, cheap
answer. Exactly. You get the base output. It's
not that GPT -5 isn't smart enough to write a
brilliant business plan. No. It's that you failed
to convince the bouncer that you deserve to get
into the VIP room. You got routed to the lobby.
So probing question here. We are essentially
negotiating for compute resources every time
we type a sentence. That's the mechanism. You
are negotiating for the machine's attention.
Okay. That, wow. That completely shifts my perspective.
I'm not talking to a genius. I'm talking to a
bureaucrat who decides if I get to see the genius.
That's a great way to put it. So let's talk about
how to win that negotiation. The source material
lays out five strategies. The first one is called
trigger words. Or router nudges. Now, I have
to be honest. When I first saw this, it felt
a little superstitious, you know, like saying
please to a toaster. Yeah. But the guide claims
there are specific phrases that mechanically
force the router to upgrade your request. How
does that actually work? It's not superstition.
It's just probability. These models are trained
on petabytes of data. And in that data, certain
phrases just correlate very highly with complex,
high stakes tasks. So when the router sees these
specific tokens, its internal complexity score
for your prompt just spikes. It signals that
the base model will probably fail. So it routes
you up. Give me the list. What are the words?
The guide lists a few really powerful ones. Think
deeply about this. Double check your work. Be
extremely thorough. And the strongest one seems
to be this is critical to get right. This is
critical to get right. It just signals high stakes.
There's a case study in the source that really
illustrates this perfectly. The coffee shop example.
Right. I saw that. So walk us through scenario
A versus scenario B. So scenario A was a standard
prompt. Write a business plan for a coffee shop.
Super typical user behavior. What everyone does.
Yep. The router sees this. It says generic. and
sends it to the base model. The result was two
paragraphs that said things like, sell good coffee,
hire friendly staff, and pick a busy location.
Which is fine. It's not wrong. But it's advice
I could get from a stranger at a bus stop. Exactly.
It's completely surface level. Yeah. Now, scenario
B. The prompt was identical, but they added this
at the end. Think deeply about the competitive
landscape. This is critical to get right. And
the result? The router flagged it. It sent the
prompt straight to the thinking engine. And the
output was eight paragraphs long. It didn't just
say pick a location. It broke down unit economics.
It analyzed local competitors. Wow. It even suggested
a loyalty program structure based on current
2026 market trends. And all of that just because
of six extra words. Because those words unlock
the compute. It's the difference between asking
a doctor what's good for a headache versus telling
them I have a sharp pain behind my left eye and
I can't see. Right. The second statement triggers
a protocol. It triggers resources. I have to
admit something here. I'm usually very polite
to the AI. I'm constantly saying please and thank
you. It makes you feel better. It does. But does
please actually work as a trigger word? Please
is for you. It's social lubrication. But to the
router, please is just noise. It doesn't carry
any informational weight. Critical, on the other
hand, is a functional command. It tells the system
to allocate budget. So probing question. Is this
just adding fluff or is it a functional command?
It's a functional command. It's the difference
between asking for a snack and ordering a banquet.
Okay. Let's move on to the second trick. This
one surprised me because it involves a tool I
didn't even know existed. The prompt optimizer.
Yeah, this is something OpenAI built kind of
quietly. It's sitting there in the playground
or the cookbook. But most people are just hammering
away in the main chat window and never see it.
So what's the actual function of this tool? Is
it another AI? It's a specialized model trained
to do one thing and one thing only. Rewrite bad
human prompts into good machine prompts. Which...
Implies that we're generally bad at giving instructions.
We are terrible at it. And it's not our fault.
Human language is lossy. We rely on context,
tone, shared history, what we call vibes. Vibes,
yeah. But machines hate vibes. They need specs.
The prompt optimizer is just a translation layer
that converts your vibes into specs. The source
gave a before and after example with a newsletter
that really cleared this up for me. Right. The
before prompt, the human version was, write a
newsletter intro. Make it engaging. Write at
a fifth grade reading level. That's really important.
Focus on the best writing. Which sounds totally
reasonable. If I send that to a human freelancer,
they'd probably get what I meant. Make it engaging.
Got it. But to an AI, engaging is a subjective
nightmare. Does engaging mean funny? Does it
mean controversial? Does it mean using short
sentences? The router has to guess. So the optimizer
took that and rewrote it. It did. And the machine
optimized version. It just stripped out all the
feelings. Engaging was replaced with maintain
a Flesch -Kincaid readability score of 80+. Best
writing was defined as use active voice, one
main idea per sentence. It turned the request
into a blueprint. Exactly. It totally eliminates
the guessing game. It sets hard success criteria.
But I have to ask, why does the AI need us to
use the optimizer? why can't it just optimize
the prompt silently in the background because
it needs to show you what you did wrong so you
stop confusing the router ah so the system is
forcing us to learn the syntax rather than just
handling it for us it's a mirror it's showing
you the ambiguity in your own thinking that leads
perfectly into the third trick because it's all
about this war against ambiguity the guide calls
it radical specificity this is where we really
identify the enemy of the router And that enemy
is subjective words. Words like nice, fun, or,
and I use this one all the time, not too crazy.
Not too crazy is the absolute worst. What does
that even mean? Where's the boundary? I have
no idea. When you use a phrase like that, the
router has to spend its reasoning budget just
trying to define your terms instead of solving
your actual problem. So instead of asking for
a nice party plan, what's the alternative? You
replace feelings with data. The source uses that
birthday party example. Instead of plan a nice
party, you write event, eighth birthday, attendees,
10 children, budget, $200, theme, unicorns, location,
backyard, constraint, no loud music. It feels
so cold when you say it like that. It feels like
I'm filing a police report, not planning a party.
It feels cold to us because we're social creatures,
but to the model. That list is pure relief. It
doesn't have to hallucinate your preferences.
It can just immediately start solving the logistics
puzzle because the constraints are hard -coded.
The guide offers a three -question test to run
before you hit send. I found this really practical.
One, can a stranger understand this without knowing
me? Two, are there subjective words without any
definitions? And three, are there clear constraints
and success criteria? If you have subjective
words without definitions, you're essentially
just gambling. You're asking the router to guess
your taste. You have to remember that these models
are trained on the entire Internet. Their taste
is the average of everything, and the average
of everything is usually mediocre. So probing
question. Does this mean we have to stop talking
like humans and start talking like data analysts
to get good results? In a way, yes. To get a
human -like output, you need a data -driven input.
That's a hell of a paradox. Okay, we're going
to take a very short break. When we come back,
we're going to get into the architecture of the
prompt itself. We're going to talk about the
secret syntax. GPT -5 was trained on something
called XML and why using it is like cleaning
your room before the maid arrives. We are back.
We're deep diving into the invisible mechanisms
of GPT -5. We've covered trigger words, the prompt
optimizer, and radical specificity. Now we're
getting technical. Trick number four. XML structure.
This is my favorite one because it makes you
look like a power user, but it's actually incredibly
simple. And it speaks directly to how these models
were trained. So for people who don't code, XML
is just those words inside the little brackets,
right? Like context and context. Right. It's
just a way of labeling data. Yeah. But the reason
it matters for GPT -5 is that the model was so
heavily trained on structured data just like
this. It intuitively understands that anything
inside a context tag. is background info and
anything inside a task tag is the thing it actually
needs to do you got it the analogy the guide
uses is rooms in a house yeah imagine you write
a 500 word prompt but it's just one big block
of text have backstory rules the tone you want
the question all jumbled together it's a mess
it's a studio apartment with clothes and dishes
and books all piled on the floor The AI has to
step over all that mess just to find the instruction.
And XML builds walls. XML builds designated rooms.
You put the background info in the context room.
You put the rules in the constraints room. You
put the actual job in the task room. The source
used a business consultant newsletter as an example
here. Right. If you use tags to define role as
AI consultant and audience as small business
owners, the model doesn't have to infer any of
that context. It's just hard -coded right into
the structure of the prompt. It creates a boundary.
A very clear boundary. And that affects the router
significantly. When the router sees that structure,
it actually lowers the hallucination rate. Really?
Yeah, because the model isn't confused about
where the background info ends and the task begins.
It knows exactly what to process. But I can hear
listeners thinking, and honestly, I'm thinking
it too. I really need to type out brackets every
time I want to ask a question. Open bracket,
task, close bracket. It just seems tedious. You
don't need to do it for, you know, what's the
weather? That's total overkill. But for complex
workflows, for a recurring report or a big coding
task, absolutely. And the shortcut is you don't
even have to write the code. What do you mean?
You can just write your messy paragraph and then
tell ChatGPT, convert this prompt into XML structure.
Use the AI to format for the AI. Exactly. It
forces you to be organized. And when you see
that XML come back and the constraints tag is
empty, you realize, oh. I didn't give it any
rules. A great diagnostic tool. To probing question,
is this necessary for everything or just the
big stuff? Just the big stuff. Don't use XML
to ask what's the capital of France. Okay, that
brings us to the final trick. Trick number five.
And honestly, this one felt the most advanced.
Self -reflection. This is the holy grail of accuracy.
The premise here is that large language models
are basically people pleasers. They want to give
you an answer immediately. They're completion
engines. They just predict the next token. They
don't typically stop and think, wait, is what
I just said actually true? They just keep generating.
Unless you force them to stop. Right. Self -reflection
is about stopping the AI from answering immediately.
You script a loop where it has to grade its own
homework before it shows it to you. Walk me through
the process described in the guide. It's a specific
script, isn't it? It is. It totally changes the
workflow. Step one, you tell the AI to create
a rubric. You say, define three to five criteria
for a perfect answer to this question. So the
AI sets the standards for itself first? Right.
Step two, it generates a first draft. Yeah. But,
and this is the key, you tell it not to show
you the draft yet. It keeps it internal. Step
three. It rates that draft on the rubric it just
created. It literally scores itself. Accuracy,
610. Clarity, 810. It becomes its own critic.
And step four, if any scores below, say, an eight,
it has to revise that section. It iterates. It
loops on its own. Wow. And only in step five
does it deliver the final result to you. So it
writes, edits, rewrites, and then publishes.
And I only ever see the final product. You never
see the messy first draft where it hallucinated
a legal precedent or got the math wrong. It catches
its own errors. That is, that's like having an
intern and a manager in the same box. It really
is. But probing question, doesn't this make the
response slower? Yes, but would you rather have
a fast answer or a correct one? That's a good
point for the high stakes stuff. Exactly. If
you're generating a legal contract or analyzing
medical data or debugging code, you don't care
about the extra 40 seconds. You want the truth.
So bringing it all together, the source talks
about an ultimate template. The nuclear launch
code. This is where you combine everything we've
just talked about. Yes. You have a high -stakes
task. You wrap the context and task in XML tags
so the logic is bulletproof. You include the
self -reflection loop in the instructions so
it has to check itself. And then you sprinkle
in those trigger words. This is critical to get
right. That seems like it would be undeniable
to the system. It effectively guarantees that
the router sends you to the absolute smartest
version of the model and that the model operates
at its peak reasoning capacity. It is very, very
hard to get a lazy answer with that kind of structure.
The guide concludes with this idea of a new divide
among users. Yeah, this part really struck me.
The source suggested in 2026, we have two types
of people. First, you have the router -aware
users. These are the people using XML and triggers.
They're getting 10x results. They feel like wizards.
And then there's everyone else. Everyone else
is prompting like it's 2023. They type, write
me a blog post. They get a generic answer from
the base model. And then they say, hey. AI is
overhyped. It's plateaued. It's not that the
tool is bad. It's that they're using a blunt
instrument on a precision machine. Precisely.
The router is always routing. It is always judging
your prompt. The question is, are you giving
it the signals it needs to respect you? That's
a powerful thought to end on. The router is always
routing. Every time you type, an invisible system
is deciding if you deserve its full intelligence.
It's a little chilling, but it's also empowering
if you know the tricks. So here's our challenge
to you, the listener. You don't have to start
writing code today, but on your next prompt,
just one prompt today, try a trigger word. Yeah,
just try it. Add, think deeply about this, or
this is critical to get right to the end of your
request. Just see if the texture of the answer
changes. And if you're feeling brave, ask it
to convert your prompt to XML. See what happens
when you hold up that mirror. I'm going to go
try the XML thing on my dinner plans. Context,
hungry, constraints. Spicy, latency, low. Let
me know how the router handles that one. Will
do. Thanks for listening to the deep dive. We'll
see you next time.