When you dedicate time to building a complex
custom AI tool, say a specialized data scraper,
it is well, it's going to have flaws. It always
breaks down. But the real shift here is how we
approach failure. The dilemma is how do you fix
messy data, map outputs and debug logic without
writing a single line of code? And the core insight
from the sources we looked at is that debugging
is now simply a matter of conversation. You just
described the error to the AI, and it updates
its own logic. Welcome to the deep dive. If part
one was about getting a working prototype, this
is part two. We're taking that idea and turning
it into a polished, production -ready tool that
can handle real -world complexity. Our mission
today is to really analyze the five key troubleshooting
steps from the source material. We're going to
look at how conversational prompts in AI Studio,
plus some workflow refinement in 8AN, can eliminate
traditional debugging. Yeah, moving from prototype
to reliable automation. Let's get into it. So
in the first build, we confirmed the raw signal
was received by NEN. That part worked. But the
data itself is messy. It's just a big block of
text. Right. And now the challenge is organization.
How do you make sure specific data points land
in the right spreadsheet columns? This is the
first big hurdle. It is. This is challenge one.
Mapping the webhook data to your sheets. When
that raw payload arrives, it's just this undifferentiated
mass of information. We need to define exactly
which piece goes where. So if I have a column
named date in my Google Sheet, I need to tell
the system precisely which incoming field should
map to it. Precisely. You're inside the ANAN
Google Sheets node. And on the left, you see
all the raw input fields, maybe 20 of them, like
generated date or company name. And on the right
are your spreadsheet columns. That sounds incredibly
tedious if there are 20 or more fields. Is it
a drag and drop thing for every single one? It
is. It's a one -time setup that needs that manual
alignment. You drag, say, search city from the
input, and you map it to the city column on the
output side. So if the initial data is flowing
correctly, what's that critical step to ensure
it lands in the right place? It's all about field
mapping. That ensures the raw inputs correctly
align with the spreadsheet columns. Okay, so
that alignment is the first step toward production.
You're forcing the unstructured AI output to
fit into your structured database. You have to
make the data talk to the database. Exactly.
This is where things get really interesting.
Once that mapping is done, we immediately hit
a failure. Challenge two, missing social media
data. The AI just didn't do a perfect scrape.
LinkedIn and Facebook fields were empty. A perfect
example of what developers call prompt drift.
Prompt drift. Yeah, it's when the AI kind of
forgets or deviates from a critical instruction
that was buried in that original setup. And I'll
admit, I still wrestle with prompt drift myself.
I often forget the small details in my own instructions
that can throw an entire search off. We all do.
So how do we fix it? Not with code. You go right
back to the AI Studio chat interface. The user
just described the problem in plain English.
What was the actual fix prompt? It was conversational,
but really direct. Something like, great. I love
it. But we don't have all the data. We are missing
social media. Please make the AI agent better.
Also use Google search and Google Maps properly.
But wait a minute. Telling an AI to... use Google
Maps properly, isn't that just replacing complex
code with, you know, a different kind of vague
instruction? That's the key difference. You aren't
coding the search path yourself. You're asking
the AI to refine its own internal logic. The
AI translates that human instruction into a more
accurate search sequence. And it worked. It instantly
started populating those missing fields. OK,
that is powerful. Now, challenge three, the timing
issue. The app was sending a webhook call for
every single lead, which is just inefficient,
terrible for performance. Oh, definitely bad
practice. And the fix, again, was a simple conversational
prompt. Help me make a fix. When the data will
be sent to the webhook, I want all of it to be
sent in one single batch, not one by one. And
just that one sentence changed the entire structure
of the AI's output. Yep. The AI understood, and
it restructured its output into a single clean
payload. It was an array containing all the scraped
leads delivered at once. So how do you efficiently
correct a faulty data retrieval process? You
just refine the prompt in chat. that compels
the AI to improve its internal search logic.
So now we have this high -quality batch of data
arriving cleanly, but that creates challenge
four. The data is now one big array. How does
NEN split that up into individual rows for Google
Sheets? Right. The sheet needs five separate
records, not one giant chunk of text. Exactly.
This is where you restructure the workflow. The
solution is adding a dedicated step, a split
-out node. Right after the webhook in an ANN.
A split -out node. I love how literal that is.
It is. It takes that bundle batch of, say, five
leads and splits them into five distinct sequential
items. You can kind of think of it like stacking
Lego blocks of data. But does adding another
node slow things down? Is there a hidden cost
here? It adds negligible processing time, but
it's absolutely essential. The Google Sheets
node can only write one row at a time, so the
split -out node makes sure it runs five times
once for each lead. It makes the whole thing
modular. Okay, so moving to challenge five, adding
advanced features. Can we do more than just fix
bugs with conversation? Absolutely. The source
shows adding a lead count selector. The prompt
was just add another form input, lead count.
I want to be able to select between 10, 20, 30,
up to 100 leads to be scraped. So the user just
described the UI they wanted and the interface
and the logic updated on its own. Instantly.
The AI studio added the drop -down menu with
the exact options specified. It shows you can
iterate on front -end design, features, and back
-end logic all conversationally. So how do you
handle a bundled payload of multiple leads when
updating a spreadsheet? A split -out node is
required to process the batch into separate actionable
items. This all defines a really powerful new
iteration pattern, doesn't it? It's test, identify,
describe the fix in plain English, the AI updates,
and then you test again. That conversational
refinement is the central idea. It replaces that
long, painful debug cycle with just immediate
iteration. And this AI Studio plus N8n stack
is a universal framework. The leaf scraper is
just one example. Let's talk about those other
applications. If I'm not scraping leaves, what
else could I build with this? Okay, think about
a content creation suite. The AI app generates
a draft blog post, maybe three social media snippets,
and a prompt for graphic. And AN handles the
distribution. Exactly. NN posts the draft to
WordPress, schedules the snippets in Buffer,
and saves the assets to Google Drive. The AI
creates. N8N distributes. What about for internal
stuff like customer service? You could build
a customer support analyzer. The AI app monitors
tickets, extracts the sentiment, suggests a reply.
Then the N8N side updates your CRM. If a ticket
says urgent, NAN sends a Slack alert and changes
its status to critical. It connects the intelligence
to the actual operations? It does. So is this
iterative process limited to data scraping alone?
It sounds like no. No, this pattern functions
as a universal framework for building any custom
automation tool. Now that the app works, where
does it live? The sources mention some surprisingly
accessible deployment options. You've got three
clear paths. First is just personal use, keeping
it in your AI studio account. Second is team
sharing. You just share the app's URL with your
colleagues. And the third option is properly
public. Yeah, public deployment. This is where
you deploy to Google Cloud with a custom domain.
People are actually launching these as standalone
web apps, even getting them to rank in search
engines without ever hiring a developer for the
backend. Okay, let's get into some pro tips.
Even if we're just chatting with an AI, what's
the one habit we have to maintain? Prompt engineering
still matters. The AI is a powerful tool, but
you have to be a specific director. Don't just
say send the data. Say send the data as a single
JSON array. And what about testing? Test incrementally.
Do not write a five -page prompt trying to define
everything at once. That's just guaranteed to
fail. Start simple. Verify the input form works,
then add the webhook. Test that. Build and test
one feature before adding the next. The sources
also mention using the chat for small refinements.
Absolutely. The chat is for quick UI tweaks like
make that button blue. Yeah. And your safety
net tip four is the NAN executions panel. Can
you explain what that panel actually shows us?
It is your debugging truth. It's where you can
visually see the raw data moving between each
step so you can pinpoint the exact spot where
things failed. So if I want to avoid massive
failures, what is the best building technique?
Build and test incrementally, verifying one small
feature before adding the next. We have to address
the cost. Building custom tools is usually expensive.
But this whole picture seems surprisingly affordable.
The free tiers are phenomenal. Google AI Studio
gives you a generous free API quota. And NA Cloud
offers, get this, 20 ,000 workflow executions
every month for free. 20 ,000 executions a month.
That's a staggering amount of automation for
$0. Whoa. I mean, imagine scaling a system efficiently
entirely on free tiers for all your initial testing
and even moderate personal use. It really democratizes
this for everyone. So when do you actually start
paying? When you hit high volume. Exceeding Google's
API quotas or needing more than those 20 ,000
executions on NAN, their plans start around $20
a month. And of course, hosting on Google Cloud
if you deploy publicly. Let's talk about the
pitfalls. Besides over -prompting, what other
traps did the sources highlight? Skipping the
data test. That's a fatal error. You have to
test that webhook connection first to make sure
data is actually arriving. And another one is
ignoring error handling. Tell the AI what to
show if a search fails, like no leads found.
Try different keywords. And there was a practical
tip about the destination, Google Sheets itself.
Yes. Keep your Google Sheets simple. Data connectors
in general get really confused by complex formatting,
merged cells, frozen rows. Clean data needs a
clean destination. So besides being specific
in prompts, what helps ensure NAN runs smoothly?
Keep your Google Sheets clean. Avoid complex
formatting that could confuse the data connector.
So we've established the live build wasn't perfect.
It had stumbles, data mapping errors, timing
issues. And that's the best news. The key takeaway
is that every single one of those problems was
solved purely through conversation. No debugging
Python, no hunting for syntax errors. The power
is in that loop. see the problem, describe the
fix in English, and the AI handles the rest.
It's just dramatically faster. This stack really
is a fundamentally different, more powerful way
to build. You don't need expensive sauce subscriptions
or to compromise on features anymore. You just
need a clear idea and the language to define
it. A functional, powerful lead scraper was built
live, including all these fixes in under 30 minutes.
The true power here is that conversational iteration.
It transforms complex bugs into simple chat requests,
and it makes sophisticated automation accessible
to anyone. So the sources leave us with a thought.
What will you build in your next 28 minutes?
Considering this flexibility, a content suite,
a support analyzer, what is the most complex
business workflow that could realistically be
managed entirely by just talking to an AI agent?
Yeah, think about that one problem you've been
putting off because it seemed too complex to
code. Maybe now it's just a conversation away
from being solved. Thanks for diving deep with
us. We'll see you next time.