Most people type eight words into ChatGPT. They
copy the generic answer. Then they just close
the tab. It saves a few minutes. But it doesn't
change your income, Pete. But top operators,
they treat AI entirely differently. They treat
it like a business partner, one that works while
they sleep. Welcome to the deep dive. Today we
are exploring a really fascinating playbook.
It was built from observing over 50 founders.
We are breaking down custom AI business systems
from multi -model arguing to automated finance
reviews. Yeah, it completely reframes how we
use these tools. I have to admit something upfront.
I still wrestle with prompt drift myself. Getting
the AI to actually stay on track is hard. OK,
let's unpack this. It is a very common struggle.
You want it to do the heavy lifting, but you
have to build the system first. You have to fundamentally
fix how you talk to the machine. You do this
when you are awake. So it actually works when
you're asleep. Exactly. Let's start with what
we call the Google trap. We treat AI like a simple
search engine. We expect absolute magic from
incredibly short prompts. We hit Enter and get
frustrated. Right. It is a massive misunderstanding.
Think about bringing a brand new human advisor
in. You bring them into your office. You look
at them and just ask, Should I raise prices?
Without giving them any context at all? Exactly.
You give them zero background, they would completely
fail or give a textbook answer. It sounds exactly
like a generic Wikipedia summary. AI works the
exact same way. The solution is providing the
full context first. You have to give the model
the messy background. You do this before you
ask for any decisions. You share your current
business stage. You give it last month's monthly
recurring revenue. So you provide your current
churn rate too. You list the options you're actually
weighing. You share your 90 -day goals. And crucially,
you show your real fears. Yes, that last part
is so important. Let's pause on that for a second.
Why does giving the AI our real fears actually
change the output? Well, because it stops the
AI from giving Wikipedia summaries. It forces
the model to weigh emotional and business risk.
It pulls from crisis management instead of generic
theory. So provide the messy background, not
just the final question. Right, once you do that,
things definitely shift. But even with perfect
context, there is a major risk. One single AI
model still has dangerous blind spots. You really
cannot trust the first answer. Confident models
can be flat out wrong. They might miss something
totally obvious about your market. This brings
us to the three model sequence. You use three
different models in sequence. Step one is the
draft phase using Gemini. Gemini is best for
fetching fresh web information. It gathers the
raw materials you need quickly. Step two is the
critique phase with Claude. Yeah, Claude plays
devil's advocate here. It looks for logical flaws
or generic text. It actively finds where the
logic falls apart. Step three is the finalize
phase with ChatGPT. You paste the original draft
and quad's feedback in. You ask ChatGPT to rewrite
it into a professional style. Let's look at a
high stakes pricing strategy example. You use
this sequence to battle test the logic. You do
this before deciding on your praising tiers.
But setting up three different models sounds
exhausting. Aren't we just multiplying the hallucination
risk by using three models? Uh, not if you do
it correctly. They aren't collaborating to invent
new facts. They are actively instructed to argue
and fix each other's biases. It creates a robust
system of checks and balances. Right. Pit the
models against each other to catch their blind
spots. Two -Sec Silence. Sponsor, replace holder.
Getting models to argue is highly effective,
but typing out all those rules every time is
exhausting. Opening a blank chat over and over
is slow. The system desperately needs memory.
This is where workspaces come into play. We're
talking about Claude projects and skills. Yeah,
and we should probably define skills here. Reusable
instruction packs the AI only loads when it needs
them. Perfect. Early data shows a massive shift
with these. Teams cut repetitive drafting cycles
by up to 87%. They do this just by using these
built -in skills. You completely stop re -explaining
yourself to the machine. You need three crucial
files to stop generic writing. The first one
is the anti -AI style file. You have to ban phrases
like, in today's fast -paced world, ban dive
in and game changer, ban unlock the power of.
Why do models love those phrases so much? They
are statistically over -represented in the training
data. The model just defaults to the most probable
next word. The second file is the voice profile.
This dictates short sentences, mostly under 14
words. You use you way over I. And you absolutely
ban hedging words like maybe or perhaps. Exactly.
The third file is the fact dossier. This is the
unchanging truth about your business, your exact
prices, stats, and your audience. It's like stacking
Lego blocks of data to build a custom brain.
But let me ask you this. If I only have time
to make one file today, which is the most critical...
Without a doubt, the fact dossier, tone can always
be tweaked later. But made -up numbers ruin your
audience's trust instantly. Give the AI permanent
memory so it actually sounds like you beat. Once
the AI has your permanent memory locked in, you
can take your hands off the keyboard entirely.
You can do this for highly repetitive weekly
tasks. Let's talk about AI business agents. An
automated workflow that checks data... drafts
text, and notifies you. Exactly right. You just
reclaim 5 to 10 hours a week. Automating Monday
loops or Friday inbox sweeps is powerful. You
review the final output and approve it. This
leads us seamlessly into something called vibe
coding. Vibe coding changes how we build digital
tools entirely. You describe what you want in
plain English. You use tools like cursor, lovable,
or bold. Let's talk about the Duolingo chess
app example. It was built by just two people.
They had no coding or chess experience at all.
And they hit seven million daily active users
in six months. The core lesson here is crucial.
When AI fails, don't rate a fancier prompt. You
feed it a database of real examples, like those
real chess puzzles they used. Whoa! Imagine scaling
to a billion queries without knowing how to code.
What's fascinating here is the underlying lesson.
The core point is mastering the iteration loop
itself. Why shouldn't I wait until I fully understand
a market before building? Because the new playbook
is build while you learn. Shipping ugly and looping
five times beats a six month polished launch.
Every prototype teaches you faster than reading.
Build ugly, test with real data, and automate
the boring loops. Two secs silence. Building
and automating is obviously incredibly powerful,
but the system is not fully complete yet. You
must rigorously review the final output. And
you have to review the ultimate bottom line.
Let's talk about using Gemini for content review.
Don't just use AI to write more text. Use it
to actually cut more. Ask Gemini to find the
three weakest sections of a script. And write
a better two -line hook instead. Right. Catch
those structural problems early. Then we have
the monthly finance check. Run this check on
the 1st or the 15th. You drop revenue, costs,
refunds, and churn into clod or perplexity. You
ask what changed and which products improved
margins. You ask what hidden risks exist right
now. But remember, AI is an educator, not a licensed
CPA. Don't ask it to pick stocks for you. Use
it to turn a three -hour finance dread session
into a 20 -minute reality check. Is it really
safe to dump raw business financials into a chatbot?
You use privacy -focused enterprise modes for
this? And you scrub any personally identifiable
info first. Focus on the ratios rather than exact
bank account numbers. Exactly. Look at the margins,
not the raw routing numbers. Use AI to map your
money and slash fluff. But consult a pro. Eat.
If we connect this to the bigger picture, the
shift is profound. We are moving away from transactional
AI usage. Right. One simple prompt giving one
generic, boring answer. We are moving rapidly
toward infrastructural AI. workspaces, multi
-model reviews, permanent files, and automated
agents. It really becomes the true backbone of
the business. Remember the rule of compounding
here. Do not try all nine steps this week. Pick
just one single step to start today. If you write,
build the three permanent voice files first.
If you sell a product, set up with a monthly
finance review. Do it consistently for 30 days
before moving to step two. I want to leave you
with a final reflection. If vibe coding and agents
make generating content and code nearly free
and instant for everyone, what happens to the
value of human taste? When anyone can build an
app in a weekend, the advantage shifts entirely
from the speed of your typing to the quality
of your curation. Something to think about. Out
to your own music.