Hey everyone, and welcome back to a deep dive.
It's really awesome getting all the source material
you send our way, covering everything from, well,
all sorts of stuff. Today, we're diving into
something way more practical for a lot of you,
I think. If you're wrestling with automation
workflows, maybe using an Enan and spending just
way too much time figuring out why things aren't
working, this one's for you. We got this great
article. Four hidden NN errors that waste your
time and quick fixes to solve them and it's got
some really good stuff in it Yeah, it really
pinpoints some common pitfalls the kind that
you know make you want to pull your hair out
sometimes Because the workflow looks right, but
it just won't cooperate the article promises
These are the mistakes that can waste hours,
maybe even days But once you know about them,
the fixes are surprisingly pretty fast It's all
about getting automation to actually save you
time right not create more headaches Totally,
yeah. So our mission today is to really unpack
these four specific errors, the article highlights.
We want to understand why they happen, what they
look like in practice, and most importantly,
nail down those quick fixes the source provides.
We're going to dive right in and try to extract
those core insights for you. And you know, understanding
these isn't just about debugging one tricky workflow.
It's kind of bigger than that. It's about building
more robust, reliable automation systems. right
from the start. It's about getting those fundamentals
down that make a big difference in the long run.
Okay, let's unpack the first big one the article
calls out. It's dealing with messy, unstructured
text data. Right. Like imagine you're getting
customer inquiries through email or maybe a basic
web form and all the details, a person's name,
their email address, the actual message just
jumbled together in one big block of text. You
need to pull just the name or just the email
out automatically. How do you even begin to do
that effectively? Nod's slowly. That's a classic
challenge, isn't it? a really common one. People
often go down the road of trying complex pattern
matching, like regular expressions, regex. And
the source points out that while regex can work
sometimes, it's often pretty rigid. It really
struggles with variations in how people phrase
things or format their text. Plus, let's be honest,
regex can be kind of intimidating to learn and
maintain. Right. Oh, yeah. And the article has
this really interesting solution here, something
you might not immediately think of for this task.
It's using ChatGPT, but not really for conversation.
It's using it as a data parser. Here's where
it gets kind of cool. Exactly. The source lays
out a pretty simple process for setting this
up in an NaNChatGPT node. You basically tell
the AI its job using the system message. You
say something like, your task is to extract specific
information, like first name, last name, email,
and the customer's message from the text I provide.
OK, so that's like the job description for the
AI. And the messy text goes into the user message
field, right? That's your input. Precisely. That's
where you feed it the actual email body or the
form submission text, whatever it is. And the
absolute key, the real trick from the article,
is the assistant message. This is where you provide
a blank JSON template that shows ChatGPT the
exact structure you want the output in, like
first name, last name, email, message. Oh, OK.
So you're not just asking it to extract info.
You're showing it how to structure the extracted
info. That makes sense. The article even mentions
you can ask ChatGPT itself to generate that blank
JSON template for you by just describing the
variables you need. It's pretty cool. It really
is a neat shortcut and the final critical step
the source mentions for this node is making sure
the output content as JSON setting is turned
on. This ensures ChatGPT formats its response
strictly as JSON. Once it processes the unstructured
text, you get back clean structured data in that
JSON format you specified. And that clean JSON
data. Yeah. That's going to be super easy to
then use in other nodes, like mapping it directly
into columns in a Google Sheet or maybe fields
in a CRM node. Exactly. What's fascinating here
is how it leverages AI's natural language understanding.
It can deal with variations, typos, or just different
phrasing in a way that traditional rule -based
methods like regex just can't really handle well.
It's just way more flexible for real -world messy
text. So if you're spending ages manually copying
and pasting info from emails or form submissions
or maybe struggling with complex text formulas,
using Chat GPT as a data parser like this...
guided by a JSON template. Think about where
you deal with unstructured text, emails, support
tickets, maybe online reviews. This method, according
to the source, could save you a ton of manual
work or complex setup. That's like a major aha
moment for how to use AI differently. That's
a total paradigm shift for text processing and
automation, really. All right. Let's move on
to mistake number two. The article identifies.
This one's about Google Sheets, super common
tool. Say you've got customer data, maybe those
leads you just parsed, neatly in a sheet. You
want to build an interactive system where you
can check a box next to a customer's name, maybe
like a ready -to -contact box, and have that
instantly trigger an action in an 8N, like adding
them to an email sequence or something. How do
you make an 8N actively listen for that specific
check mark change happening right there in the
sheet? People often get stuck wanting an 8N to
constantly check the sheet. This is a really
common scenario where people hit a wall. They
want that immediate reaction, right? That connection
between a human action in the sheet and the automated
workflow, but they don't know how to create it
efficiently. NNN isn't designed to just sit there
constantly asking Google Sheets to anything change.
How about now? Now, after a few seconds. Right.
That would be incredibly inefficient. Lots of
wasted resources. So the sources solution here
sounds a bit technical at first glance. Combining
web hooks with a little bit of Google Apps script,
which It might make some people's eyes glaze
over initially, but the article insists it's
actually easier than it sounds. It really is
much simpler in practice than the names might
suggest. The article uses a great analogy. Think
of a webhook as a special phone number assigned
to your NAN workflow. Instead of your workflow
constantly calling Google Sheets asking for updates,
you give Google Sheets this special number. Then
you set up a tiny piece of code in Google Sheets
using Apps Script that basically says, hey, if
anything gets edited here, call this phone number,
the webhook URL, and tell NA10 about it. Gotcha.
So Google Sheets becomes the one making the call
when something relevant happens instead of NAN
polling constantly. Much more efficient and instantaneous.
Exactly. That's the key. The steps from the article
are, first, you set up a webhook node in N8n.
It gives you a unique URL. That's your special
phone number. You copy that. Then you go into
your Google Sheet, find Extensions in the menu,
and click Apps Script. This opens up a script
editor right in your browser. And you paste a
specific piece of JavaScript code that the article
provides, right? There's usually a template for
this. Making sure to replace a placeholder in
the code with that unique webhook URL you copied
from N8n. Correct. That code typically uses the
onEdit function, which is triggered automatically
whenever the sheet is edited. And it uses something
called EarlFetchApp .Fetch to send the data about
the edit to your NEN webhook URL. Once the code
is in, you just set up a simple onEdit trigger
within the ActScript environment itself, usually
by clicking a little clock icon over on the side.
This just makes sure that onEdit function runs
every time someone edits the sheet. OK. And then...
back in an AN, after the Webhook node receives
the information about the edit, you need a way
to make sure the workflow only continues if the
right thing happened. Like, not just any edit,
but that specific checkbox getting checked. Precisely.
That's absolutely crucial. That's where you add
a filter node immediately after the Webhook node
in your NAN workflow. The data sent by the Apps
Script usually includes details about the edit,
which cell changed, what the new value is, maybe
the old value too. You configure the filter node
to check for specific conditions. Did the change
happen in, say, column E? And was the new value
true? Or whatever your specific condition is.
Oh, right. Because you don't want any random
edit in the sheet, like fixing a typo, to trigger
your important email workflow. Just checking
that specific box. That filter is key. Absolutely.
It's the gatekeeper. Without it, just saving
the sheet or typing anything could potentially
trigger your workflow, which you definitely don't
want. Wow. OK. So that combination. the NA10
webhook node getting the call, the simple app
script making the call on edit, and the NA10
filter node checking the specific condition that's
how you create that instant interactive connection.
You can then add whatever action node you want
after the filter, like a Google Mail node to
send an email or maybe create a task, your project
management tool. That unlocks building truly
responsive systems driven by actions directly
in your spreadsheet. That's pretty powerful.
It really connects the dots between human interaction
within a common tool like Sheets and sophisticated
automated workflows in a very powerful real -time
way. All right. Mistake number three. The source
talks about people hitting a wall or maybe just
feeling intimidated. When they encounter the
HTTP request node in N810, it just looks kind
of technical, right? Like maybe there's this
fear of HTTP requests. Yeah. It can definitely
seem like this scary abstract thing you should
probably just avoid if you can. But here's the
reality, and the article points this out really
clearly. Every single pre -built integration
node you use in ANN, the Google Sheets node,
the Slack node, the Telegram node, whatever it
is, they're all just prepackaged simplified HTTP
requests under the hood. Oh, really? So when
I'm using a Google Sheets node to add a row,
ANN is basically just formatting. and sending
an HTTP request to Google's system behind the
scenes. Exactly. It's just dressed up in a nice
user -friendly interface with specific fields
for you. And so when you need to connect to a
service that doesn't have one of those nice pre
-built nodes, you have to use the generic HTTP
request node. That's fundamentally how systems
talk to each other over the internet by making
these kinds of requests. OK, so it's fundamental,
not just something for like... coding experts.
But how do you figure out what request is sent?
That looks super complicated with URLs and headers
and bodies and authentication and all that stuff.
That is the pain point, figuring out all those
specifics. And the source offers a great solution
to make it way, way less scary. It involves leveraging
the service's API documentation and using the
import curl feature within the NAN HTTP request
node itself. API documentation. Okay. Yeah, that
sounds even more scary. Maybe scarier than HTTP.
Calmly. Let's go back to the article's analogy.
It's a good one. Think of an API application
programming interface as the waiter in a restaurant.
You, your NAN workflow, are the customer. The
service you want to talk to, like an SMS sending
service or maybe an AI voice generator, like
11 Labs, which the article mentions that service
is the kitchen. You don't need to go into the
kitchen and figure out how they make the food.
You just look at the menu, that's the API documentation,
and tell the waiter, the API, what you want to
order. OK, waiter analogy helps. The API docs
are the menu. So the HTTP request is how I place
my order with the waiter from the menu. Precisely.
And the brilliant shortcut that the article reveals
is that most API documentation, most menus, include
examples of how to make requests using something
called cURL. cURL is just a common command line
tool for making HTTP requests. The examples in
the API docs will look like a line of code starting
with the word cURL, something like the example
the source gives. cURL dash x pst summerl dot
ch, header info dash d data payload. You don't
need to understand what all the cURL command
arguments mean. Right, okay. The article says
you just find that example request in the service's
API documentation, look for sections called API
reference or developer docs, or just examples
showing cURL, and you copy that entire line of
code. That's the magic part. You copy the whole
thing, the whole cURL command. Then you go back
to the HTTP request node in NAN. Instead of manually
filling in the URL, the method, like posty, puty,
et cetera, the headers, the body data, which
is super tedious and really error -prone, you
just find the import cURL option in the node
settings. the curl code you copied and hit import.
And N8n automatically parses that curl command
and populates most of the fields in the node
for you, like the URL, the method, headers. Yes.
It's a massive time saver and honestly an error
reducer. N8n figures out the URL, the HTTP method,
Parity, GT, etc. puts the right stuff in the
header section and formats the data for the body
section all from that one line of curl code.
Wow. That is a major shortcut, seriously. So
you don't have to manually figure out all those
technical fields. The only thing you usually
do have to add yourself, according to the article,
is the authentication part, right? Like your
personal API key or maybe a token. Right. Authentication
is usually unique to your account, so it's typically
not included in the generic curl example you
find in the docs. You'll need to add that manually,
usually in the authentication tab of the node
or sometimes as a specific header, just following
the specific instructions in that service's API
documentation, the menu. But importing the curl
takes care of like 90 % of the complexity. It
completely unlocks your ability to connect to
literally thousands of services that don't have
pre -built end -to -end nodes, all just by knowing
where to find the curl example and how to import
it, suddenly no more fear of the HTTP request
node. OK, that makes it way less scary. So find
the API docs, spot the curl example, copy it,
import into N8n. That's the trick. That's huge.
Precisely. It democratizes API access quite a
bit. All right, last one. And the source calls
this the silent killer. This is something that
apparently causes huge frustration because your
workflow looks logically correct. You set up
everything right step by step, but it's just.
doesn't work, often with really vague errors
or sometimes no error messages at all. It just
fails silently. The article says this is often
caused by not understanding JSON data types.
Absolutely fundamental for reliable workflows,
and yet it's subtle. The problem isn't necessarily
the data itself, like the value true or the number
123. It's about how that data is formatted and
stored its type. If a node expects one type of
data, like a number, but it receives another
type, like text, it can behave unpredictably
or just fail silently. The article breaks down
the five main data types in JSON, and importantly,
how N8 then helps you visualize them with little
icons. Okay, let's quickly go over the five main
types mentioned. and what they look like in N8N
based on the article. First, string, which is
just text. Right. That's just plain text data,
like a name. Nguyen Man Em was the example, an
address, an email. In any N, you'll see a little
A icon next to it representing text. Number.
Pretty straightforward. Yep. For numerical data,
you can actually do calculations with, like an
age, 34, a quantity, 100, or maybe a price, 100
.5. That gets a hashtag icon in N8N. OK. Boolean.
which is true or false. And this is crucial.
This is the actual Boolean value, true or false.
Always lowercase, no quotes. In AN, it has a
distinctive on -off switch icon. This is critically
different from the text strings true or false,
which would have quotes if you saw them raw.
And importantly, they get the A icon in AN. OK.
So the text true with an A icon is completely
different from the Boolean true with the switch
icon. Got it. That seems important. Exactly.
And that distinction causes so many subtle problems
in workflows. OK. Then there's Object. That's
like a container for related data. It holds a
collection of key value pairs, often represented
by curly braces in JSON. Think of it like a folder
holding related pieces of information, like all
of a customer's contact details together. And
finally, Array. That's simply a list, an ordered
collection of items, often represented by square
brackets in JSON, like a list of tags, maybe
urgent, follow -up, or a list of items in an
online order. Okay, so string in A, number, hashtag,
boolean switch, object array, and the article
gives that super clear example of why types matter.
Number two plus number two equals four, but the
text two plus the text two equals 22. Totally
different outcomes depending on the type. Yes.
It's fundamental to how programming and automation
handle data manipulation and comparison. And
the most common frustrating example, that silent
killer the source points out, is exactly that
filter condition check we talked about earlier.
If you have a filter node checking, if a value
called, say, is verified equals the Boolean true,
expecting the switch icon. But the data coming
into that node actually has is verified stored
as the text string true, which would show up
with the A icon. instead of the actual boolean
true with the switch icon. Exactly. The filter
condition is verified equals true will always
evaluate to false, even though you're looking
right at the data and it says true. You'll be
sitting there for hours, maybe days, thinking
your logic is broken or the filter node is buggy.
It's because you're comparing a text string to
a Boolean value and they are simply not the same
thing to the computer. You might get a weird
error sometimes, but often you just get no output
from the filter node and you have no idea why.
Oh man, I can totally see how that would be a
silent killer. It looks right to the human eye,
but fails fundamentally for the machine. That's
got to be maddening. So the fix the article gives
is actually pretty simple once you know what
to look for. Pay attention to the data type icons
in NA. Precisely. This is the core insight here.
NAN explicitly shows you the icons next to the
data in the output panel. If you see an A icon
when you were expecting an on -off switch icon
for a true -false check or maybe a hashtag when
you needed an A, you immediately know you likely
have a data type mismatch issue. That's probably
your problem. And the solution isn't usually
writing complicated code to fix it right. The
article says many nodes, like the filter node
or maybe the set node, have a built -in convert
types option. Yes. Often it's just a checkbox.
You just turn that on and NAN will often automatically
try to convert the incoming data type to match
what the node expects or what you specify in
the node settings. So it can try converting the
text true to the boolean true or maybe the text
123 to the number 123 before the filter or calculation
happens. Wow. Just knowing to look at those little
icons, the A, the hashtag, the switch, the brackets,
the braces, and knowing that a mismatch is probably
the problem. problem if things aren't working,
and that there's often a simple convert types
checkbox that feels absolutely fundamental to
building reliable workflows. It can fix so many
of those frustrating head -scratching problems
that give you no clear error messages. It truly
is a critical shift in perspective when you're
debugging. Don't just look at the values, look
at the types, pay attention to those icons. Okay,
so we've unpacked the four big ones the article
says caused the most pain. The messy text data
issue, and using ChatGBT to parse it cleanly.
struggling to track Google Sheets changes instantly
and how webhooks combined with Apps Script provide
that real -time trigger, being intimidated by
HTTP requests but using the brilliant import
curl trick to simplify things massively, and
finally the frustrating silent killer of not
understanding JSON data types and the importance
of checking those little icons in ANN. Right.
These are presented in the article as the most
common sources of major frustration, the things
that really waste time. But as we've discussed,
the good news is, once you identify them, the
fixes aren't usually overly complex. And understanding
these core issues helps you build much more predictable
and reliable automation systems from the get
-go. They let you build workflows that actually
work consistently, not just sometimes. Yeah.
Because, like we said right at the top, automation
is supposed to save you time, not make you spend
days or weeks debugging. Avoiding these common
mistakes, the article points out, should really
help workflows actually deliver on that promise
for you. So maybe as a final thought for you
to mull over, drawing from the source, out of
these four potential pitfalls the article outlines.
The messy text extraction, the Google sheet tracking
setup, the fear of HTTP requests, or the JSON
data type confusion. Which one feels most relevant
to challenges you've personally faced in your
own automation work, or maybe anticipate facing
soon? Thinking about that specific area and really
focusing on applying the fix or the approach
we discussed, where could understanding just
that one thing potentially save you the most
time and frustration right now moving forward?
It's just something to consider, I guess. That's
a really good point to consider. A great takeaway.
This has been a super insightful deep dive into
some really practical N8N problem -solving techniques.
Thanks again for sharing the source material
that made this possible. Really valuable stuff.
Always interesting to unpack these practical
insights and find those aha moments. that can
make a real difference. Definitely. Keep exploring,
keep automating, and we'll see you on the next
Deep Dive. Sounds good. See you.