So if you're using AI just to write slightly
better emails, or maybe summarize some text,
you might be missing out on, well, a lot. That
kind of basic stuff is fine, sure, but it really...
barely scratches the surface of what's possible
when you get into more complex work. That's really
true. You know, when you look at any real project
like building software, planning a marketing
campaign, even something physical like a carpenter
building a table, it all follows this fundamental
pattern. Input, then processing, then output
IPO. And our goal really is just to maximize
the value of that final output. Systematically
using AI helps us do that. Welcome to the Deep
Dive. Today our mission is to help you turn AI
from maybe a random chat partner into a real
systematic partner for design for strategy. We're
pulling the best ideas from the source material
we looked at on how to build these powerful workflows.
Yeah, we've got a pretty clear path laid out.
First, we're going to properly define that IPO
structure, input, processing, output, what it
means. Second, we'll talk about the essential
tools you kind of need and some setup tips you
can't skip, especially around AI memory. That's
key. And finally, we'll walk through two really
specific examples using AI and product design.
and then for creating social media content at
scale. Okay, let's unpack this IPO model first
then. The simple brilliance here is that AI can
actually help at all three stages. It's not just
something you plug in at the end to maybe clean
up your writing. It can speed things up everywhere.
Exactly. So start with input. This is where AI
tackles, well, the messiness of real life. Tools
like VoicePal or Grain were mentioned. They're
important because they take messy, stuff -spoken
ideas, rambling voice notes, maybe an hour -long
meeting, and turn it into organized text the
AI can actually understand and work with. They
handle that transcription and maybe some initial
source. Right. So that cleaned up data then moves
into processing. This is like the central hub,
the brain of the operation. You need a strong
foundation here. That's why something like Claude
was recommended. What's important about Claude
or similar advanced models is that big context
window. Yeah. And that context window is absolutely
essential. It holds the memory for a complex
project. It does the heavy lifting. organizing,
checking data fast, planning things out, even
drafting, all while keeping the project's history
sort of in mind. And then get the output. This
is where the finished thing comes out. Polished.
We saw some neat specialized examples. Firecut
for video edits or Gamma turning text drafts
into presentation slides pretty quickly. So the
toolkit kind of mirrors the structure. Right.
Input tools for cleaning up, central processing
brain, and then output tools for getting it done
fast. OK. So thinking about that central processing
hub, the one holding all the project memory.
Yeah. What would you say is the absolute most
crucial first step before you even start a complex
project? Ah. You have to teach the AI who it
is and what the mission is. Precisely, yeah.
If you want the AI to act like a professional
partner, you absolutely have to personalize it.
The most critical setup step is making sure that
memory is solid. This really means avoiding having
to explain your job or the company voice or the
whole project background every single time you
log in. That step is so vital because otherwise
the AI just defaults to generic textbook answers.
Right, right. And speaking of context, another
key setup piece is file uploads. You need to
feed it your own data, PDFs, spreadsheets, style
guides, so it can check things against your actual
real -world situation. your constraints. Mm -hmm.
You're basically setting up a clear data structure,
not just randomly dumping files in. You're telling
the AI, this is the hierarchy of knowledge for
this specific task. You know, I still... Well,
actually, I still wrestle with prompt drift myself
sometimes. I remember trying to map out a new
hiring plan in a chat where I'd just been drafting
like... casual messages to friends. And suddenly
the AI started talking about aligning core values,
but using like really aggressive slang. Total
meat of the tongue. Got completely confused.
Oh, yeah, that's a classic problem. And that's
exactly why the source material really emphasized
the project's feature tip. You've got to create
separate, dedicated spaces, like one called Q4
marketing strategy, another personal blog ideas.
It keeps the information quarantined, stops the
AI from getting rixed up, keeps things focused.
So when we use these dedicated projects, these
separate spaces. Yeah. What's the key confusion
we're stopping the AI from making? It stops the
AI from blending your different job contexts
together, keeps work separate. Okay, this is
where it starts to get really practical. Let's
test this theory. Workflow one, using AI as a
product manager. Let's imagine we're designing
a simple budget app for students. We'll call
it student money. Right. So step one needs really
clear instructions in the prompt. Define the
user, the persona. You tell the AI to act like
an expert user researcher. And you ask for specifics,
deep specifics, target audience. Vietnamese university
students may be 18 to 22 years old. And crucially,
you define their main pain point. Maybe they
feel lazy and just avoid writing down every single
expense. That emotional detail matters. Yeah,
if you don't give it that level of emotional
context about the user, the AI will probably
just give you a generic feature list, not a real
solution. Exactly. Then step two is designing
the user flow. Based on that lazy persona, let's
call him Anne, you prompt the AI to design the
absolute easiest flow possible. Goal, add an
expense in under five seconds. So maybe the AI
guesses the category. If Anne types fuk long,
does it just assume coffee? OK, but hang on.
If we make it that simple, just relying on guesswork,
don't we lose accuracy? How do we make sure Anne
isn't just tagging everything as coffee and maybe
missing big things, like rent or a tuition payment?
That's the exact question you posed to the AI.
You ask it to analyze that specific trade -off.
You set the hard constraint under five seconds,
and the AI has to suggest the compromise, how
to balance speed and accuracy. See, it shifts
your job from designing every little screen yourself
to basically challenging the AI's design assumptions.
Ah, OK. That makes a lot of sense. So we've defined
the user, the flow, debated the trade -offs.
Mid -roll sponsor, read, placeholder. Welcome
back to the Deep Dive. We've got our user Anne
and the quick expense adding flow defined for
our student money app. What's step three? Ah,
right. Ask for a simple model. A prototype. So
we instruct the AI to create a clickable prototype,
but we specify exactly what screens we need,
and the key elements, like home screen needs
a huge O plus button, input screen, only show
the number keyboard, keep it minimal, and then
maybe an automatic done screen after input. Yeah,
this is where the AI does that translation, takes
those abstract rules you set and turns them into
something tangible, something visual you can
react to. You're not painstakingly drawing boxes
and arrows, you're giving the system the rule
to design for you. And then we hit step four,
test and fix. iteration, we challenge the AI's
prototype, throw an edge case at it, like, how
does this super simple app handle a loan Anne
gave to a friend? We make the AI analyze the
trade -off again. Do we add complexity, like
a loan section, or does that break the core simplicity
needed for Anne? And the big lesson here, really,
is you don't need to be a coder. You don't even
need to be great at wireframing tools. You just
need to be incredibly clear about describing
the requirement, the problem you're trying to
solve for the user. The AI handles the translation
into a testable model. So what core function
is the AI really performing in this whole design
loop then? It quickly translates abstract ideas
into a tangible model for review. Okay, let's
switch gears. Workflow 2. AI as a content strategist.
Thinking about fast platforms like TikTok or
Reels, often the hardest bit isn't making the
video. It's coming up with ideas that actually
connect, that resonate. Step one here is about
getting emotionally effective hook ideas. We
don't just want generic topics. The prompt needs
to tell the AI acting now as a social media marketing
strategist to generate maybe 20 ideas and then
crucially sort them by psychological triggers
that would work for our user and things like
pain point, curiosity, quick win. I really like
that focus. It forces the ideas to be relevant
right from the start. Something like why you're
always broke by the 20th and how to fix it in
30 seconds. hits a pain point, offers a quick
win. Exactly. Then comes step two, which is critical.
The human filter. You have to apply your feeling.
The AI gives you 20 psychologically sorted ideas,
yeah. But you only pick the best, maybe three
to five that genuinely resonate with you, that
feel right for your voice. You're the curator
here. Wow. Just imagine scaling that. Testing
thousands of those kinds of psychologically angled
concepts in like minutes, that speed for strategic
planning, it's kind of mind blowing compared
to traditional brainstorming. It really is a
total acceleration of that early creative phase.
Okay, so now step three, creating a detailed
script. Right. You take one of your chosen hooks,
let's use this while you're always broke by the
20th, and you prompt the AI for a full, say,
30 second script. The key here is using a structured
format, like a table, time column, words column,
visual column, and you need specific visual directions
in there, like zero three seconds, close up shot
of your face looking serious, or 10, 15 seconds,
fast cuts showing three small, impulsive buys
coffee snacks. Which leads perfectly into step
four, and this is maybe the most important point,
adaptation. Do not just copy and paste that script.
Please don't. Read it. Understand the structure,
the flow, but then you absolutely have to adapt
it. Use your own voice, your tone, your humor.
The final video, the final content, it must feel
like it came from you, a real person. Beyond
just making it relatable, why is keeping your
own specific voice so crucial in that final step?
What risk are we avoiding? Authenticity ensures
the content feels human. Avoid sounding generic
or predictable like an algorithm wrote it. So
just to quickly recap the big ideas for you listening,
AI really shines when you integrate it properly
into that input processing output system. That
random one -off use, it needs to evolve. Yeah,
and critically, AI shifts from just being a tool
to being a real partner, like a product manager
or a content strategist, but only when you give
it super detailed role -specific instructions.
You got to treat it like a highly skilled con...
and Sultan you've hired, be specific. And these
workflows we talked about, they're really just
the beginning. The source material hinted at
more advanced stuff. Things like using AI to
understand feedback from thousands of customers
automatically, or even building out a whole online
course structure from just a basic outline. Pretty
powerful extensions. So here's a final thought
to chew on. If you get really good at defining
the problem perfectly, and defining the user
persona with deep clarity, and setting those
strict rules for the AI to execute the plan,
Are you still the creator in the old sense? Or
have you become something else, maybe a highly
effective curator of intelligence? Definitely
something to think about. We really encourage
you to stop using AI just randomly. Start thinking
about and maybe building your own IPO systems
today. Thanks so much for joining this deep dive.
Until next time.