Building things with AI. Well, it's never really
been easier, has it? The technical side, getting
an app running. That barrier is almost gone now.
Yeah, it's pretty wild. But here's the thing,
the paradox. Loads of new AI apps launch, right?
And most just fizzle out, like almost instantly.
It's a huge graveyard out there already. It is.
And the source we're digging into today really
nails why. It's usually not the tech itself.
It's this mindset. They call it vibe coding.
Vibe coding. I like that phrase. It captures
that idea, that sort of false sense that building
a product is easy, almost lazy, like you can
just run on excitement. Exactly. It completely
skips the actual hard work. The really fatal
mistake is thinking you can just bypass understanding
what the user is actually struggling with. So
the crucial skill, it's shifted. It's not about
elegant code anymore, is it? Not primarily, no.
The real job now, the critical part, is defining
a painful problem that's genuinely worth solving.
And you've got to do that before you even think
about building anything. Right, so today we're
doing a deep dive into this three -stage framework.
It's a blueprint designed to help you avoid that
exact failure trap, taking that raw idea and
turning it into something real. Yeah, we're going
to unpack it all. Stage one, using AI for some
deep market research, finding that sweet spot.
Stage two, taking that data and making a proper
plan, the product requirements document, or PRD.
Okay. And then stage three, the actual rapid
AI build, and crucially... Testing it in the
real world to see if it sticks. The mission really
is turning those cool ideas into products people
will actually open their wallets for. Okay, let's
start there at the foundation. Understanding
before building. The big reality check is just
how cheap and fast building has become. Oh, totally.
The cost to get something functional out the
door, it's plummeted. But that changes the game,
right? The challenge isn't the tech execution
anymore. It's proving people actually want this
thing. Proving product market fit. That's the
bottleneck now. So stage one means a shift in
thinking. You stop being just the builder, the
programmer, and you kind of become a detective.
You're investigating the crime scene, which is
the market, looking for the smoking gun. The
smoking gun. A real customer pain point. Yeah.
The thing that current solutions aren't quite
fixing or aren't fixing well enough. Got it.
And you use AI tools for this detective work,
but strategically. Not just asking broad questions.
You've got to use the deep research modes in
tools like ChatGPT, Gemini, Claw. Right, and
focus on getting authentic, unfiltered feedback.
Exactly. Where do you find that? Places like
Reddit, specific review sites for the industry
you're looking at, professional forums maybe.
That's where people vent, right? They complain
about the tools they're currently using. That's
gold. Unprompted frustration. Exactly. The source
uses this example of building an English learning
platform. The prompt they used for research wasn't
just tell me about language apps. No, it was
super specific, like analyze competitors like
Babel, list the most common user pain points,
what features are missing, and crucially, what
makes someone switch platforms. Really detailed
stuff. That detail is everything. The AI gives
you back a report. Sure, competitors, feedback.
But the absolute goldmine in that report, it's
the feature gap section. Yes, that's the key
insight. That tells you exactly where you can
create real value, make something different,
something genuinely better that people might
actually pay for. You stop guessing. You start
building what the market explicitly said it needs.
So the whole point of stage one, the ultimate
goal, is validation. Finding that clear proof,
the smoking gun, that your idea tackles a real
painful, maybe even expensive problem that people
are actively complaining about. If they're not
complaining, maybe the problem isn't painful
enough. Right. Makes sense. So committing to
finding that smoking gun first, how does that
really save you time down the line? Well, it
stops you building something nobody actually
wants or needs. Saves you months on a useless
product. Okay. Makes sense. So that leads us
right into stage two, the blueprint phase. AI
-powered product prep. The source uses this master
chef analogy. Right. You've got your ingredients
from the research. Now you need the recipe card.
Exactly. This is where that raw research becomes
a concrete plan. The specs for development. And
you have to remember that G .I. Go principle,
garbage in, garbage out. A fuzzy idea leads to
a fuzzy product. Every time. You need detailed,
research -backed requirements if you want a focused,
useful app. And the core recipe card here is
the PRD, the product requirements document. That's
the blueprint. It needs, what, five key sections?
Yeah, five non -negotiables. One, brief description.
What is it? Two, target audience. Who's it for?
Use those research personas. Okay. Three, core
functionalities, like the absolute main three
things it does to solve that pain point. Crucial.
Four, the user journey. How do people find it,
sign up, start using it? And five, basic design
guidelines. Keep it simple, but guide the look
and feel. And the real power move with the prompt
you use to generate this PRD is you make the
AI connected back to the research. You say, draft
this PRD and justify why we need each feature
based on specific findings from stage one. That
closes the loop. Research drives the plan. I
have to admit something here. Even after doing
this a bunch, I still wrestle with prompt drift
myself sometimes. Trying to get those big ideas
into tight practical requirements. It's easy
to lose focus. Oh, totally. And that's kind of
what the PRD structure helps fight, right? It
anchors the AI, keeps it on mission. But you
also need more than just words. You need visuals,
too. That's a great point. The PRD tells the
AI what to build. Yeah. But showing it screenshots
of apps you like, apps that look good, that shows
the AI how to... lay it out visually. It's like
pointing at a picture on the menu for the chef.
Exactly. Keeps it from looking generic, ensures
it feels professional. So thinking about that
PRD blueprint, if you had to pick the single
most critical part. Definitely nailing down those
core functionalities, the ones that directly
solve that primary user problem. Nothing extra
yet. Okay. Stage three then. Execution and validation.
Taking that blueprint, sending it off to the
3D printer, the AI development platform. Yeah,
exactly. Time to actually manufacture the thing
fast. We're talking about these modern vibe coding
tools here, the ones built for super rapid web
app development without needing deep coding skills
yourself. Right. The goal isn't replacing traditional
developers entirely. It's about speed to test
an idea in the market. Precisely. And the initial
build. It's shockingly quick. You feed it the
PRD, you give it those visual examples, and you
can get a working first version, often in under
five minutes. Neatly. Including things like login
and a basic dashboard. Yep. Authentication, onboarding
flow, dashboard structure. Because it's using
pre -built components and understands the PRD's
meaning, it's fast. Wow. okay then what then
you iterate it's this conversational refinement
loop you just chat with the ai add a settings
page or tweak this button updates take like three
to five minutes usually and it keeps the style
consistent across the whole app automatically
yeah that's the beauty of it maintains consistency
and you can add really high value stuff easily
like for that language app example you can just
ask it to add listen buttons for pronunciation
audio or even integrate a record function so
users can speak and get instant ai feedback Things
that used to be complex projects. Whoa. Okay.
Imagine iterating like that. Adding serious features
so quickly. It really is like snapping together
Lego blocks of functionality. That speed itself
is a huge advantage. It's the new edge. Absolutely.
But speed doesn't mean skipping the quality checks.
You've got to stress test this thing. Right.
What specifically? Check the login works reliably.
Click through all the core features. Test any
audio or video stuff. Make sure data is saving
correctly. Treat the AI like a junior dev partner
almost. Find bugs. Describe them clearly. Ask
for fixes. Makes sense. Document and iterate.
Then before you show it to anyone outside. Final
polish. You can use hybrid tools point and click
for small visual tweaks or chat commands like
change the whole color scheme to warmer orange
tones. Okay. And critically run the platform's
built -in security scans. Always do that before
any public exposure. All right. Now the really
crucial part, the smoke test. You have this initial
build. Now you have to see if anyone actually
cares, right, before you invest more. Exactly.
Real -world validation. Start simple. The waitlist
test. Spin up a nice landing page. Takes minutes.
Use AI to write some compelling copy based on
your research about the pain point. See if people
actually sign up to be notified. Okay. That tests
initial interest. What's next? The ultimate test,
really. The pre -order test. integrate payments
early, stripe, whatever, offer a discount for
pre -ordering, maybe a lifetime deal or the first
month. This tests if people will actually pay.
That feels bold, but I see the point. It validates
the business model, not just the idea. Precisely.
And finally, the usability test. Get that first
version, the minimum viable product, the MVP,
in front of actual target users. Watch them use
it. Can they figure it out? Do they get the value?
Does it actually solve their problem like you
thought it would in stage one? That full circle.
So why is asking for money early, that pre -order
test, considered the most definitive validation?
Because someone pulling out their credit card
is the strongest signal you have that the business
model itself is viable. Okay, let's pull back
a bit. Think about the bigger strategic principles
here. What's the mindset needed? First, you got
to be obsessed like a product manager. Your focus
isn't perfect tech. It's solving that real user
problem you found, relentlessly. And how you
work with the AI. Treat it like a partner, not
a magic wand. It's conversational development.
You give the high -level strategy the why and
the what. The AI handles the how, the implementation
details, it's collaboration. Which implies embracing
that rapid iteration idea, right? Speed over
perfection initially. Absolutely. Get something
out fast, learn from real users, and be totally
willing to pivot based on the data. Don't get
attached to your initial assumptions. Learn and
adapt quickly. And practically speaking, don't
just rely on one single tool. Right. Pros use
a tooltip. Maybe ChatGPT or Clod for the deep
research. A specialized AI platform for the actual
build. Maybe even a code -focused AI if you hit
a snag and need to debug something specific.
If one tool hits a wall, export the code, use
another. Got it. And thinking long term. These
rapid prototypes are great, but a sustainable
product needs more, yeah. For sure. You need
a roadmap driven by user feedback. You need to
think about performance, security over time.
And crucially, you need a real business model.
How do you find users? How do you keep them?
That's beyond the initial build. So if building
is becoming easier, almost commoditized, where's
the competitive advantage now? What's the new
moat? It's not how you build anymore. The real
defensible moat comes from that deep understanding
of the problem, your strategic thinking, your
speed of execution and learning, and just being
relentlessly focused on the user. And looking
back at that AI as a partner idea, what's the
most common trap people fall into there? Thinking
the AI can read your mind or that it's infallible,
assuming it's magic instead of providing clear
strategic direction and then validating its output.
Okay, so wrapping this up. The winning formula
seems pretty clear then. It's about obsessing
over real problems, using AI smartly to build
and learn faster, and always, always focusing
on delivering real value to the user, not just
cool tech. That's it. And the blueprint is straightforward.
Find a real problem, research it deeply, plan
strategically with that PRD, build fast with
AI prototypes, and validate constantly, especially
willingness to pay. Feels like the future really
belongs to people who start with a problem they
genuinely get, maybe something they've experienced
themselves. I think so, too. That personal insight
combined with AI's power for super fast testing
and iteration, that's the massive opportunity
right now. The chance to solve meaningful problems,
potentially at a huge scale. Exactly. So for
everyone listening, maybe it's time to put on
that detective hat. Start your own investigation
today. Find that problem.