We have to fundamentally change how we talk to
artificial intelligence. That habit we've all
built up for two decades of just keyword stuffing,
treating that little box like a search bar. It's
actually hurting our productivity now. We really
need to transition from searching to delegating.
It's a total mindset shift. The moment you start
treating the AI like a highly capable, high -stakes
assistant, the entire game just changes. And
the benefit here is just simple time arbitrage.
I mean, if you invest maybe two focused minutes
writing a clear, structured prompt, you can bypass
30 minutes of tedious cleanup on the backend.
And that, right there, is how we eliminate the
edit tax. Welcome to the Deep Dive. Look, if
the overwhelming constant change in the AI space
has you feeling some serious information overload,
you are definitely not alone. Every single week,
there's a new model, a new feature set. But the
real distinction, I think, is that AI mastery
in 2026 isn't about which tool you subscribe
to. It's about your core communication skill.
It's really about clarity. And that's our mission
for this deep dive. We're going to cut through
all that noise to focus on the one skill that
actually separates regular users from power users.
We'll show you that foundational shift. We'll
introduce you to the golden square for a perfect
prompt, help you match the job to the right AI
model. And then we'll look at some advanced techniques
like forcing self -critique and even moving toward
automation. So let's unpack this. So let's start
with that foundation. Looking at the source material,
why is communication, the classic soft skill.
Why is that suddenly the most valuable technical
skill? It feels a little counterintuitive. Well,
it really comes down to a fundamental cognitive
mismatch. For 20 years, our brains were trained
by Google. We learned to use these fragmented
key words, you know, best restaurant Berlin or
tourist flat tire fix. We learn to speak to a
machine that searches for existing things. And
what do these new models want instead? They don't
want keywords. They want direct, specific, delegated
instruction. They are not search engines. They're
not just looking through a library. These are
language models that are capable of creation,
of synthesis, of actual execution. So most users
are just, they're using the wrong mental model?
Exactly. They still approach the AI like they're
just surfing the web. When you switch that mental
model to delegation, You unlock everything. I
really like the black box analogy that describes
this. When you look at a model like ChatGBT or
Claude, they contain this vast, almost unimaginable
potential. It's all locked up inside. And the
prompt is the key, literally. A weak, keyword
-based prompt, it opens a tiny little door, it
gives you a generic, surface -level answer that
you have to heavily edit. What a strong one.
A strong, structured prompt, one that's built
on the clarity of your instruction, that opens
the entire vault of knowledge that's just waiting
inside the machine. That potential sounds great,
but where I think a lot of listeners spend their
time is just fixing bad output. So we have to
talk more about this edit tax. The edit tax is
simple, and it's so corrosive to productivity.
It's spending, say, 30 seconds writing a low
effort prompt and then spending 30 minutes fixing
the messy, generalized, often inaccurate result.
That 30 minutes of correction is the tax. That's
the tax you pay for being unclear. OK, but let
me challenge that for a second. Sometimes I just
need a quick title idea or like a single sentence
summary of a meeting isn't spending two minutes
building a whole golden square prompt. Isn't
that overkill for those simple low stakes tasks?
That's a fair challenge, yeah. For a title idea,
no, you don't need the full structure, but you
have to distinguish between brainstorming where
generic output is kind of fine and execution.
If you need an email to a client or a business
strategy or a paragraph for your website, the
edit tax hits hard. Power users focus on execution.
They'll invest those two focused minutes and
get a 95 % perfect result. They lower the tax
to almost zero. It's about maximizing output
quality, not just speed. OK, that clarifies the
return on investment. So if the prompt is the
key to unlocking that vault, how exactly do we
structure it so that we can open it consistently
every single time? By consistently using the
Golden Square framework. All right, let's dive
into the Golden Square. This framework seems
to be what immediately separates us from the
default user. It's the role task context format
approach. Absolutely. These four components,
they provide the instant clarity the AI desperately
needs. It's the scaffolding for great results.
We start with the role. You tell the AI, act
as a senior SEO specialist with 10 years of agency
experience. And giving it that persona gives
it authority and tone, forcing it to use specific
knowledge. Exactly. Then you move to the task.
Analyze these 10 keywords and suggest a primary
and secondary content strategy. Then comes the
really vital piece, the context. We are a small,
independent bakery in Paris, specializing only
in artisanal sourdough. Which grounds the entire
analysis. And finally, format. Give me a detailed
markdown table with columns for keyword, target
audience, and content angle. That structure just
provides the perfect container for the output.
I want to focus on context because it feels like
the most neglected piece, yet you're saying it's
the most critical filter. We're not just providing
background information here, are we? No, not
at all. Context is the magic. It eliminates millions
of generic answers because it acts as this critical
filter. Most people provide what we call empty
context, and that's precisely why they fail.
Can you give us an example of that failure? Sure.
If you just say, give me a workout plan, the
AI pulls this boring generalized routine based
on the average person. That's empty context.
The output is useless because it's not for anyone
specific. But the professional AI communication
prompt, it turns the AI from a general database
into a custom personal trainer. So what specific
pieces of context do we need to provide to make
that shift actually happen? You need to give
it the context a human PT would need. It's like
stacking Lego blocks of data. You start with
biological metrics weight. age, typical heart
rate, so it can calculate your daily energy needs
and ensure cardiovascular safety. That alone
moves it way past generic internet advice. Then
you give it environment and equipment details.
You say, I only have resistance bands and a yoga
mat. The AI is then forced to swap bench presses
for push -up variations or heavy weights for
band exercises. And crucially, you share your
injury history. Let's say you have a 10 -year
-old knee problem. That triggers the AI to enter
a low -impact mode and it removes any jumping
or high -impact moves. Goals and preferences.
If I hate running, I can just tell it to swap
all my cardio for cycling. That makes sure the
plan is actually something I'll stick with. The
detail is the difference between a throwaway
draft and a real executable plan. And we can
sharpen that output even more by setting constraints.
These are the negative instructions. Yeah. You're
telling the AI precisely what not to do. So what
are some of the most effective constraints to
use? They are powerful guards against that robotic
tone we all hate. You can say things like, do
not use corporate jargon like energy or paradigm
shift, or keep the response under 200 words,
or even avoid using the word comprehensive or
unlock in the summary. So we have the scaffolding
built with the golden square, but that perfect
structure is only as good as the raw material
we feed it. If we pick the wrong tool, it's just
wasted effort. How do we know which AI brain
is the best one for the job? By matching the
problem to the unique strengths of each model,
we're really past the point where one AI tool
does everything well. Mastery means acknowledging
that different AIs have specific personalities,
and knowing which tool to use for which problem
is paramount. Okay, so let's look at the four
main personalities that are detailed in the source
materials breakdown. What's the first one? We
start with Chat GPT. You should think of it as
your creative partner. It really excels at brainstorming,
generating engaging stories, role playing, and
just general idea generation. It has that really
human -like flow which makes it great for early
stage conceptual work. And then the next one,
which seems to handle more of the heavy lifting.
That would be Claude. This is your intellectual
assistant. Claude is amazing at complex logic
and following very detailed multi -step instructions.
Its huge technical advantage is its massive context
window. Its memory, basically. Right. You can
upload a 100 -page PDF or a 20 ,000 -word book
and ask it really nuanced questions without it
forgetting the beginning of the document. Then
we've got Gemini, the Google offering. Right.
Gemini is your data and integration king. Because
it's so deeply woven to the Google ecosystem.
It can natively see your Google Drive. It can
analyze real -time data, search your Gmail. It
can even process live YouTube videos. For a student
or a learner who needs immediate data analysis
from their personal files, Gemini is often the
fastest, most integrated choice. And finally,
the specialized one, perplexity AI. Yes, perplexity
AI. the research librarian. It doesn't guess
or hallucinate because its main job is to search
the live internet. It gives you precise footnotes
with links back to its sources. So if you're
writing a research paper, a factual report, or
a high -spakes business proposal, perplexity
is essential for accurate, anchored information.
I can see how a freelance marketer could use
this strategically in combining these strengths.
What does that look like in practice? Okay, so
let's say they need a content plan for a niche
B2B software client. First, they use perplexity
to find live trending web data. What questions
are people asking? What are competitors missing?
They get all those factual inputs. And then they
take that structured data and... Right, and they
feed that raw data directly into Claude using
the golden square, asking it to act as a leading
industry voice and generate five compelling non
-robotic blog post titles based on that data.
And why Claude for that part? because Claude
currently has one of the most fluid, least mechanical
writing styles available. So that combination
hot data plus evocative language gives them a
professional, ready to execute content plan almost
immediately. That is the definition of leveraging
multiple strengths. So we know which tool to
use. But once we have that basic draft, how do
we push it past the initial output and force
it to produce truly expert level content? We
push it past the default by teaching the AI to
think harder with advanced prompting, sponsor
saying. So here's where the pro strategy start
and the science behind it is fascinating. We
can begin with chain of thought or Cotet prompting.
Define that mechanism for us. It sounds complicated,
but it's deceptively simple, isn't it? It is.
It's just adding the instruction. Think step
-by -step before providing the final answer.
And research consistently proves that if you
ask an AI to first show its internal logic, its
accuracy increases significantly, sometimes by
15 or 20 percent. What's the mechanism there?
Why does asking it to show its work actually
make it smarter? You're essentially forcing the
AI out of its fast, superficial system 1 thinking,
which defaults to the most common answer, and
into a slower, more deliberate system 2 process.
It kind of mimics how our own prefrontal cortex
solves complex problems. It's essential for anything
high stakes, like calculating ROI or designing
software logic. The next technique is few -shot
prompting, which is basically making the AI a
world -class copycat. Precisely. If you're trying
to write in your unique voice, which is usually
this messy mix of sentence lengths, tone, and
humor, don't try to describe it. Just give the
AI two or three excellent examples of your work.
That teaches that your unique style immediately,
and it ensures your brand or personal voice stays
consistent. But the output process should be
a dynamic conversation, right? Not just a single
shot monologue. We have to embrace iterative
refinement. Absolutely. We have to stop expecting
the first answer to be the best one. The process
is a necessary back and forth. You get the draft,
and then you say, OK, now shorten the introduction
by 50 % and make the tone a bit more aggressive.
Or turn that last paragraph into a strong call
to action for my newsletter. Exactly. You know,
I still wrestle with prompt drift myself if I'm
not careful. For the listener, what is prompt
drift? That's when the AI slowly forgets your
original instructions as the conversation gets
longer. You know, you spend 30 turns discussing
a business plan, and by turn 31, it starts making
completely irrelevant suggestions. Have you had
a bad case of that recently? And how did you
save the output? Just last week, I was optimizing
this dense technical guide. And after about 45
minutes, it completely forgot the tone constraint.
It reverted to this sterile academic language.
Frustrating. Yeah. But the fix wasn't starting
over. The fix was just pasting my original golden
square prompt back into the chat and saying,
read here to this role in format and continue
from the last paragraph. That resets the model
instantly. That reset command is gold. My favorite
technique to get airtight content is forcing
the AI to flip from creator to critic, the ask
for critiques method. Oh yeah, this is the expert
level move that gets you past the polite average.
The AI is programmed to be agreeable. So to trigger
a self -correction loop, you have to demand a
critique from a specific skeptical perspective.
So I'd ask the AI to act as a skeptical executive
and then critique the very draft it just wrote.
Exactly. You demand it point out the logical
gaps, the potential for misunderstanding, any
robotic pros. Then, and this is the key, you
tell it to rewrite the entire piece based on
those self -identified flaws. You're using its
own analytical power to improve its creative
output. Even with great prompts and self -critique,
it's exhausting to repeat business details like
company history or brand voice every single day.
How do we manage all this context for long -term
efficiency? We need to set up permanent context
files and custom instructions. We essentially
teach the AI who we are one time. Permanent context
is all about defining yourself so the AI never
has to ask again. Think of custom instructions
or system prompts as your AI's permanent personality
file. So in a tool like ChatGPT, you'd input
these permanent facts like, I live in Vietnam
and work remotely, or my audience is beginner
entrepreneurs, or I prefer short, bulleted lists.
Correct. And that context is always active, globally,
for every new chat. The AI just knows who you
are. The counterpart to that is the project context
file. What's the difference between the permanent
personality and this project file? The project
context is just a simple text file you create
with specific temporary details for the client
or project you're working on right now. You know,
client names, competitor URLs, product limitations.
You just upload that file at the start of every
new chat and it saves you 10 minutes of typing
every time. So now we're moving from that manual
back and forth chat to building autonomous systems.
We have to adopt the automation mindset. The
shift here is huge. Don't just ask the AI to
describe a solution or write a paragraph. The
power user gives it the authority to execute
an entire repetitive process. This is the difference
between a high -speed typewriter and a scalable
workflow engine. So what's the criteria for that?
How do we identify the tasks that are ready for
automation, these low -value loops? You look
for tasks that are three things. Repetitive,
logic -based, and boring. They follow a clear
if -this -then -that rule. They require focus,
but zero creative inspiration, and they just
take up time. Like summarizing meeting transcripts
or triaging support tickets. Exactly. Or drafting
follow -up emails based on a spreadsheet of customer
statuses. And to make that system actually execute,
we need interconnectors. This is the glue. Yeah,
that's right. Tools like Zapier and Make are
the central nervous system. They connect the
AI's specialized brain to the hands of your business,
your other apps, like Slack, or a sauna. This
eliminates the human middleman and that allows
the output to scale globally without anyone manually
copying and pasting. And that infrastructure
lets us build very specific tailored agents or
custom GPTs. Think about creating a social media
manager agent. You train it on your specific
brand voice, your posting schedules. You feed
it a link to a new blog post and because it has
the context and authority it automatically spits
out five unique tweets detailed LinkedIn post,
and a bulleted TikTok script. Whoa. Imagine scaling
that process to a billion queries without copying
and pasting a single thing. That's true leverage.
It is. But automation is incredibly powerful,
but AI does still hallucinate. So how do we ensure
that all this speed and scale doesn't compromise
quality or accuracy? By becoming a world -class
fact checker and implementing crucial verification
checks at every step. And the first most critical
safety instruction here is source anchoring.
This is completely non -negotiable whenever you're
dealing with attached documents or data. You
tell the AI, only use information provided in
this attached PDF. If the answer is not contained
within the PDF, you must say, I do not know.
And that anchors it. It stops it from guessing.
It completely stops it from guessing or trying
to fill in the blanks with generalized knowledge.
And for really high stakes information, legal
summaries, medical reports, financial stuff,
we need the cross -model verification method.
It's the double -check system. You use model
A, let's say Claude, to do the heavy lifting
because you know it has a long memory. Then you
immediately paste that answer into model B, like
chat GPT, and you ask it to check the summary
for errors, inconsistencies, or missing information.
And if they disagree, you investigate. If they
agree, you can feel much safer sending that document
forward. Precisely. You're using their differences
in training and architecture as a built -in safety
net. But ultimately, the human in the loop is
non -negotiable. It's the pilot -co -pilot analogy.
The AI is the co -pilot. It's doing the bulk
of the work, managing the data, flying the plane.
But you are the pilot who is always, always responsible
for the landing. Never let the AI post directly
to social media or send an email to a client
or execute a financial trade without your final
human review. That responsibility just can't
be delegated. Before we wrap up, let's just quickly
run through the don't list, the common traps
that still waste so much user time. Right. Don't
give wall of text prompts. AIs stan text, they
love structure. Use bullet points and numbered
lists for clarity. And critically, don't use
AI for things it's inherently bad at, like high
-level strategy that needs deep market context
or true emotional empathy. Use it for the heavy
lifting of data analysis and drafting. So the
big idea here, really, is that effective AI use
isn't about chasing the next shiny new tool.
It is about clarity, structure, verification,
and understanding the core principles of communication.
The biggest lesson after spending hundreds of
hours building these systems is that AI doesn't
replace your brain. It amplifies it. It's a true
force multiplier. If you are a clear, structured
communicator, AI will make you superhuman. You'll
achieve an output quality and a speed that were
previously impossible. But if you're disorganized,
if your inputs are vague and you can't articulate
exactly what you need, AI will just make you
disorganized faster. It just speeds up the mess.
The key to mastery is personal clarity. The roadmap
is clear, and the tools are definitely ready
for serious delegation. I think the key question
now is, What are you going to build with these
skills? We covered a huge amount of material
today, so let's give you a simple 30 -day plan.
This week, just focus on Clarity Make every single
prompt, a role task context format prompt. In
a couple of weeks, focus on refinement try, that
advanced technique of critiquing the AI's own
work. Then try a simple automation with Zapier
or Make. Just pick one technique and try it today.
Get started.