What if you could take, say, a huge stack of
raw data, maybe 500 customer emails or survey
results, read them in, well, seconds, and instantly
know exactly what your audience wants you to
build next? Yeah, and then take that knowledge,
that deep insight, which, honestly, used to take
weeks for a person to figure out, and then, boom,
generate a whole detailed, like, ready -to -sell
30 -day course structure. That's real leverage.
Welcome back to the Deep Dive. We really appreciate
you sharing these sources with us. Okay, let's
unpack this. It feels like a real shift in how
work gets done. Our mission today is really getting
into these advanced AI workflows. We're moving
way past just generating simple text like last
year. We're talking about using AI as a strategic
partner, something that helps you get real customer
insights and build complex stuff, structures
that actually work. Right. So the roadmap is
focused. First, we'll look at workflow three.
turning that messy raw data into, well, customer
gold. The good stuff, yeah. Second, we dive into
workflow four, building an entire online core
structure from essentially a messy brain dump.
And finally, we'll pull together those crucial
mindset rules, the things you absolutely need
to get right to avoid failing when you try these
more complex tasks. OK, let's start with workflow
three. This one feels like maybe the most practical,
immediate win for a lot of people. It's moving
from that really tedious job of reading, I don't
know, thousands of lines of feedback to actually
analyzing it strategically. You know that feeling,
right? Opening an Excel file with 500 survey
answers, that's usually a week of pure pain.
Oh, yeah. Total information overload. Yeah. Kills
your productivity. But AI can read all of it,
like, instantly. But here's the really key step,
the operational bit. role definition. You absolutely
have to tell the AI very clearly, act as a customer
insights analyst. You're basically hiring an
expert for that specific task just for that moment.
Okay, but if I just ask it for a summary, won't
I just get something generic, surface level?
What are the specific things we need to demand?
The output requirements make it tactical. Right.
The output needs to be super specific. You demand
the top three things users absolutely love about
the product and just as important, the top three
concrete complaints or problems they have. And
then crucially, you ask for the hidden opportunity.
OK, explain that. Hidden opportunity, what is
that exactly? That's the innovation lever. It's
basically asking for one or two smart suggestions
that may be only a tiny fraction of users mentioned,
like five people out of 500, something you totally
miss just skimming through. That's often where
the real breakthroughs are. Differentiation.
That is fascinating. But how do we make sure
we're getting the user's voice, not just the
AI's interpretation of what people might think?
Ah, good question. You have to demand real user
quotes. For every single point, every top love,
every top complaint, and especially for that
hidden opportunity, you make the AI pull out
a direct verbatim quote from the source file.
These quotes give you the context. They give
you the emotional proof. It's undeniable evidence.
Right, right. It connects the abstract finding
back to a real person's experience. Exactly.
It's the emotional proof point you need. Now,
Workflow 3 also has this really valuable second
part. It's focused just on intelligence gathering.
This is how you can learn about the market even
before you have your own product or your own
customer list. Yeah, this is a great trick, honestly.
You don't need your own customers yet. You just
analyze your competitors' customers. So we're
talking about analyzing competitor products,
specifically their reviews. Exactly. Go somewhere
public, like the App Store or Yelp or Amazon.
Find a major competitor. Then filter their reviews.
And here's the key. You're not looking for the
glowing five -star ones. You filter for only
the one -star and two -star reviews. Copy, say,
50 to 100 of those really negative comments.
Paste that right into the AI. That's your input.
So the input is all negative feedback. And the
prompt, again, has to be really specific. Absolutely.
Instruct the AI to act as a market researcher,
but one who specializes in product failure analysis.
You demand just two things. What are the top
three reasons customers absolutely hate this
competitor's product? And what are the exact
complaining words or phrases they keep repeating
over and over? OK, here's where it gets really
interesting for anyone listening, I think. This
whole process. It's basically the AI buying you
really expensive lessons, but for free. you instantly
learn the common pitfalls, the things people
hate, the fatal flaws, and you avoid making those
same mistakes yourself. Whoa. Yeah. Yeah. And
just imagine scaling that. Imagine processing
millions of public reviews across, like, every
platform, all feeding into your product roadmap
in almost real time. That's just, that's a massive
strategic advantage, undeniable. So does this
kind of negative data looking at failures and
pain points, does it actually give us better,
clearer building blocks for improving a product
compared to just focusing on positive feedback?
I'd say fixing pain points is almost always a
clear actionable win for making something better.
Yeah. OK, so we've used AI to find the market's
pain points, maybe even analyze competitor failures.
Now, the strategic question becomes, how do we
use AI to build a solution, something that directly
addresses those things people hate? This brings
us to workflow four, product creation, specifically
building an online course structure. Right. And
the biggest hurdle most people face when they
have expertise, when they know their stuff, it
isn't the content itself. They know the topic.
It's the structure, getting it organized. AI
can be brilliant at that. It's like an architect
for your knowledge. So step one is what you call
the brain dump. And you don't even need to sit
and type out a linear outline first. Nope. Just
use a voice memo tool, voice pal, or just your
phone's recorders. Fine. Go for a walk, maybe
15 minutes, and just talk messily. Stream of
consciousness everything you know about the topic
then get that audio transcribed that messy transcript
that becomes your raw input for the AI Okay,
that messy input is where step two comes in finding
the clear simple promise for the course Exactly,
you prompt the AI again But this time as an educational
marketing expert and you ask it to frame your
messy brain dump using the simple story brand
frame That's just a basic narrative structure
that really helps clarify your message, makes
it super clear for marketing. Okay, so how does
that framework break down for a course? Let's
take an example like confident speaking for shy
professionals. Sure. You define four key things.
Who's the hero? That's the student, the shy professional.
What's their problem? And this includes both
the external thing, like having to give presentations,
and their internal fear, you know, the fear of
being judged. How does this course help? That's
the Guide Your Course. And what does clear success
look like? Something measurable, like giving
a five minute presentation feeling confident.
I have to admit, I still wrestle with prompt
drift myself sometimes when I'm trying to get
AI to capture a really clear single message like
that. It's genuinely hard to boil down complex
knowledge into something that simple and resonant.
Oh, totally. And that's exactly why forcing the
AI into that story brand structure is so helpful.
It provides the guardrails, you know, it forces
clarity and stops you from slipping back into
jargon or complexity. How crucial is getting
that problem definition right, especially nailing
down that internal fear part? Oh, it's critical.
That internal fear is the real hook. A course
needs that clear message to really connect with
the student's internal struggle. Otherwise, it's
just information, right? Not transformation.
Yeah. OK. So once we have that crystal clear
promise, we move into actually building the thing,
the curriculum development, and then generating
initial sales materials kind of at the same time.
Yeah, exactly. Step three, creating the detailed
curriculum. Now, you prompt the AI with a different
expert persona. This time, it's a professional
instructional designer, BEAT, someone whose whole
job is designing effective learning materials
and core structures. And again, you have to be
precise about the structure you require. And
what kind of specifics do we need to demand here?
Well, for our example, maybe a 30 -day structure
broken down into four weekly themes. And for
each week, you demand not just the main lesson
titles, but also a non -negotiable practical
exercise, homework. Like for week one of our
confident speaking course, it might be record
a 30 -second video just saying your name in a
hobby. No editing allowed. Just do it. That makes
it actionable, measurable, prevents it from just
being passive watching. Precisely. Then, step
four is about immediate validation, maybe even
monetization. You take that full structured outline
and the promise we defined earlier and you ask
the AI to generate two key sales assets right
away, maybe a 200 -word marketing email for your
existing list, and say a 60 -second social media
video script. And that script needs a strong
hick that directly targets the student's pain
point, that internal fear we identified. OK,
so you've gone through this process. You have
this complete, beautiful, structured course outline.
The blueprint is there. What's the biggest, most
common mistake people make at this point? They
stop. They think the blueprint is the finished
building. They skip the absolutely critical step
of adding their unique content, their personal
stories, their insights, their soul, you know,
mineral sponsor, read Placeholder. These workflows
are clearly very powerful, immensely powerful,
but we also know they demand a really specific
approach, a certain working strategy. Let's look
at those rules of engagement, the things that
ensure these complex interactions actually work
and don't just lead to frustration or bad results.
Yeah, there are basically five essential principles
or rules you need to follow. First, always remember
the fundamental structure, input, processing.
output. Clearly define your raw data input. Clearly
define the AI's expert role for processing. And
clearly find the exact structure you need for
the output. That structure requirement is what
stops you from getting those vague summaries
we talked about. Exactly. Principle two. Generate
many. Filter with your brain. Never ask the AI
for the best idea. That's lazy. Ask it for 30
ideas or 50 potential lesson titles. Use the
AI for quantity. for options, then use your human
intuition, your expertise to filter down to the
best few, the three ideas that really resonate.
Principle three is about that division of labor,
isn't it? Use AI for structure. Keep your voice
for content. Think of the AI as the architect.
It draws the blueprints. Make sure the walls
are sound. But you have to be the interior designer.
You add the feeling, the unique stories, the
personal expertise that makes it feel like. Well,
like you. Makes it valuable. The sources also
really emphasize connecting different tools together.
Stacking, they call it. Yes. Connect your tools.
Stacking. This is where the real leverage kicks
in, I think. The power comes when these tools
talk to each other. It's like... stacking Lego
blocks of data, you know? Think about that flow
we mentioned. Record an idea in VoicePal, transcribe
and analyze it in Claude, plan it out in Notion,
and then maybe automatically turn parts of it
into presentation slides using Gamma. Each tool
does its specialized job really well. And the
final principle is that classic rule of computing,
really. Good input equals good output. Yeah.
Avoiding lazy commands is just vital. If you
type, write me a report about customer feedback,
you're going to get a lazy, generic report back.
But if you use those detailed, specific, expert
role prompts we've been discussing, you get an
expert -level result. Simple as that. And that
principle of being specific, of giving good input,
leads us right into the common pitfalls, doesn't
it? We need to know the mistakes to avoid when
trying to run these more complex workflows. Number
one beginner mistake, easily. Declaring, this
AI sucks, and just giving up too quickly. The
problem is almost always the command you gated,
or the way you structured the prompt. Not the
tool itself. You either tweak the role or adjust
the output requirements you set. Iterate. And
the second mistake ties back to what you said
about keeping your voice. Losing that human touch.
Right. Letting AI do everything. Losing your
soul. If you just generate content and use it
100 % unedited, robotically, your audience will
feel it instantly. It lacks personality, depth.
Never, ever just copy and paste without critical
review and editing. Add yourself back in. What
I find most fascinating here is the fundamental
shift in the human skills that are becoming most
valuable. That links to mistake number three.
People still think, oh, I can't code. That's
old world thinking now. These modern AI tools,
they don't need you to code. They need you to
be able to clearly describe what you want. Seriously,
the skill of articulating complex desires, explaining
your vision clearly, that's way more valuable
now than actually being able to write the code
yourself. And the final mistake. The efficiency
killer people often overlook. Skipping the setup,
not taking a few minutes to turn on memory features
in your AI tool, or create specific projects
or contexts. If you don't do that, the AI basically
forgets everything you told it three prompts
ago. Spending maybe 30 minutes setting up those
persistent contexts can save you hours of repeating
yourself and re -explaining things down the line.
Okay. So we've really dug into these very practical,
powerful workflows. What does this all mean?
What's the big takeaway for our listener here?
Well, I think the first thing to really internalize
is AI isn't magic. It's not a magic wand you
just wave. It's a super assistant, a very, very
capable assistant. So the ultimate goal, maybe,
of integrating these kinds of workflows, it's
about letting the AI handle, say, 80 % of the
repetitive, boring structural work, like analyzing
those 500 reviews or drawing up those initial
blueprints so that you, the human, can can focus
100 % of your limited time and energy on the
20 % that really matters. The strategic decisions,
the creative insights, the unique content, the
genuine connection with your audience. Exactly.
And the true power shift that these complex workflows
really are real is this. The ability to articulate
and clearly describe what you want, your vision,
your audience's needs, the precise output you
require that is now the highest leveraged skill
in the market. Full stop. So maybe think about
where you need to improve your descriptive skills
today. Thanks again for sharing the sources for
this deep dive. It was fascinating. Glad to hear
your music.