Hey, I'm Mukundan. This show is about solving
real problems with small, useful AI, things that
you can actually use today. Each week, we pick
one problem, build a simple workflow or tool,
and talk through the decisions, what to automate,
how to check quality, and how to make it reliable.
If you're a builder, creator, or just AI curious,
you'll leave with the steps that you can copy
tonight. You know those quiet moments before
a project begins, when the cursor blinks on a
blank screen and you just sit there, waiting
for the spark? That's how this all started. No
big idea. No vision board. Just curiosity and
a little frustration. I was looking at a dataset
at work. A big one. Customer transactions across
multiple months. And I realized something that...
stopped me in my tracks. I didn't know what to
ask. Sure, I could slice it by week, by product,
by region as well. But that wasn't insight. That
was just navigation. And it hit me. If I, someone
who lives and breathes analytics, sometimes struggle
to know where to begin, what chance do others
have? Like people on the business side maybe,
right? And that moment became the seed of an
idea. Could I build an AI that thinks like an
analyst? Not one that spits out charts, but one
that asks better questions. Hey everyone, I'm
Mukundan. And this is Data and AI with Mukundan.
This show is about learning AI by building it.
No jargon, no fluff, just honest stories, practical
tools, and a bit of curiosity along the way.
Today's story is special. It's the story of a
tool that started as a side project and turned
into something that taught me more about thinking
than any analytics course could ever do that.
And it's the story of the AI that thinks like
an analyst and how I, by building it, changed
how I see curiosity, data, and even... creativity
itself. Let's look at the problem behind the
problem. Here's what most people don't talk about.
The hardest part about data analysis isn't SQL,
Python, or data visualization. It's asking the
right question. You can have all the best tools
in the world, the Tableaus and the Looker, Power
BI, whatever, right? But if you're solving the
wrong problem, you're just wasting time. And
I found myself doing that quite a bit. I'd jump
into cleaning, filtering, building charts. And
only halfway through, I'd realize that my question
wasn't the one that mattered. That realization
sent me down a big rabbit hole, right? And I
started reading about exploratory data analysis.
Asking friends in data science how they would
approach it. The best analysts I knew weren't
the fastest coders. They were the ones who would
pause before touching the data. They'd say things
like, let's define what success means first.
Or what decision are we actually trying to make
here? And that was it. That's what I wanted to
teach my AI as well. Not to answer, but to ask.
So I did what, you know, I would, I think most
people in data would do. I opened my laptop and
I mean, my most people, I mean, the people who
would know how to use Python and more specifically
Streamlit, which is like a front end streaming,
front end web based library, which would help
you to create these amazing. and short web applications,
which is very easy to make. So I opened my laptop
in this instance, started my streamlet, and wrote
that first line of code. So when you build something
like this, it's tempting to focus on the shiny
part. The UI, the charts, the GPT output. But
I wanted to focus on the experience. The first
prototype that I had here was rough. You'd upload
a CSV file and the app would use GPT -4 to scan
the column names and generate about 10 questions
about the data. At first, it was basic. What's
the average? What's the trend? Nothing exciting.
But then I started adding prompts that would
mirror a human curiosity. Things like, which
variable seems most unpredictable? If this were
your business, what metric would you track weekly?
Where might hidden seasonality exist? And suddenly
the tone shifted. It didn't feel like a chatbot
anymore. It felt like a mentor. The kind of curious
teammate who nudges you towards better thinking.
And one person who actually tested this said
that it's like the app that is teaching me how
to explore, not just analyze. That's when I knew
that I was onto something. Let's look at that.
You know, technically speaking, here's what happens
really under the hood. The app reads the metadata,
column names, types, maybe a small preview of
values. Now, instead of sending the entire data
set, which could expose the PII, it summarizes
the schema, converts it into a structured context.
and passes that to GPT -4. Then GPT crafts 10
thoughtful domain -agnostic questions, tuned
for exploration. Not answers, just sparks. Later
versions added an objective field, a little text
box where you could say, I'm exploring customer
churn, or I want to find high -value users. The
AI would then adapt its curiosity to match your
intent. That was version 2. But by version 4,
I was obsessed with more security. I tend to
want people uploading data at all. So I built
a no -upload version as well, wherein you paste
the schema or sample rows locally. That converts
it into embeddings and sends only abstracted
info. Here's what, you're not having any PII
at this point. No data leaks, no corporate nightmares.
Because here's what can happen, right? You are
feeding the AI some kind of sensitive information
these days. And you may not realize that it may
be more of a negligence case. You know, you're
trying to paste something in ChartGPT or Cloud
or whatever. And this is not an internal company
tool. This could be something that you just open
your regular ChartGPT and type in over there.
But what you may not realize is sometimes you're
training their models. And by providing the data,
like let's just say you're working for a company
and the company name appears over there in the
data set. Yeah, you're probably trading the data
for sure. And when you're doing that, it can
be a problem for the company. The company would
be looking at it very differently. Maybe they
find you doing that and there could be consequences,
which I don't think, I don't know if most companies
have gotten to that stage yet, but. I feel like
it might at some point. So the whole point is
to avoid that kind of scenario. So you're just
sending out a few samples and remove those PII's.
And this is why it hit me that this isn't just
like a coding challenge. It's about designing
trust. Now, let's look at when AI became a mirror
to me. Something strange started happening as
I started testing it. Every time the AI asked
a question, I felt this urge to defend my assumptions.
It would say something like, why do you measure
engagement that way? And I'd catch myself thinking,
because that's how we've always done it. And
that right there was the point. The app was making
me reflect on my own thinking. It wasn't telling
me that I was wrong. It was making me curious
again. You know how as kids we ask why about
everything? Why is the sky blue? Why does 2 plus
2 make 4? Why can't I eat dessert first? Somewhere
in adulthood, we lose that instinct. We replace
curiosity with correctness. And this project
reawakened that childlike curiosity in me. In
a world of dashboards and KPIs, that's quite
rare. The biggest lesson this AI taught me wasn't
technical. It was philosophical. Curiosity isn't
something you have. It's something you practice.
Every time you face a new problem, curiosity
is the courage to say, I don't know that yet.
Let's find out. Now, in data analysis, this means
resisting the urge to rush to a conclusion. It
means sitting with uncertainty long enough to
understand what the data is actually trying to
tell you. And I started applying this outside
of work too. Instead of reacting, I started asking
questions in daily life. When something frustrated
me, I'd ask, what else could this mean? Now,
when I got feedback, I'd ask, what can I learn
from this? That same mindset. Help me make my
AI better. We often talk about teaching AI to
think like humans. But what if we flipped it?
What if the goal is to help humans think a little
more like AI, patiently, systematically, with
curiosity, but also humility? The line between
human and machine curiosity is... thinner than
it seems. Now, AI asks questions without ego.
Humans sometimes, well, they don't. And maybe
that's the balance we need in the age of intelligent
systems. Not machines that replace us, but ones
that remind us to stay curious. Now, when I built
this project, I thought it was a data tool. But
looking back, it was a reflection tool. It showed
me that asking better questions isn't just the
key to better data. It's the key to better decisions,
better creativity, and maybe even better living.
If there's one thing I want you to take away
from this episode, it's this. Don't just build
tools that save time. Build tools that make you
curious again. Because curiosity is the hidden
engine of every great analysis, every insight.
and every invention. And if you ever feel stuck
with data, with work, or with life, maybe what
you need isn't another answer. Maybe what you
need is a better question. Well, thanks for listening.
You can try the AI app yourself. It's linked
in the show notes. And if you linked... Well,
I mean, if you like this show, if you like this
story... share it with someone who's forgotten
what curiosity feels like before you go i want
to take this time to thank my affiliate partners
riverside fm which i use for recording my podcast
with its amazing ai editing features where i
can edit my podcast and remove any kind of cider
.ai for any research i wanted to do with the
topics i want to talk about and of course my
podcast hosting platform rss where i host my
podcast and distribute it to multiple channels
and using their dynamic ads feature anybody can
get paid if you just have 10 downloads a month
so just 10 downloads a month is all you need
to start making money and well that's it from
this episode i'm mukundan and this is data and
ai with mukundan see you next hey it's mukundan
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