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All right, here we are in Nitn and we're
looking at this chat GBT- like interface
and I'm just going to tell it to create
this workflow. And I said that I need a
workflow that will receive a form
submission via web hook. We need to
process information like the person's
company, their budget, and their project
description. We're going to use that
information to qualify them as a hot,
warm, or a cold lead, and that will be
decided by doing some research on their
company, seeing if they're in the right
industry. We're also looking for budget.
And finally, we want companies that are
looking to implement custom AI solutions
in their business. After it's done that
analysis, I want these leads to be
submitted into our CRM and ClickUp. I
also want a Gmail notification and I
want a Slack notification. So, it
thought about it. It searched through
all the nodes that it has access to and
then it gave us a little outline. So,
I'm just going to go ahead and approve
this plan and now it's building that for
us. Wow. So, not only did it spit out
this workflow that looks correct, on the
right hand side, it gives us a setup
guide. So, it tells us these six things
still need to be configured and then it
also tells us how to use this and
activate it and actually push it into
production. And so if I click into a few
of these nodes here, you can see that
they're actually configured already.
Besides needing to connect to our
ClickUp account, we have variables being
passed through this entire flow. So
we've got the name right here, but then
you can also see we're sending over
content like the lead info as far as
company contact email phone
different information about the lead.
It's giving us an AI analysis and a
qualification level and also a score out
of 100. And then down here, it's
assigning a priority. And then after it
updates that row in our CRM, it uses
this set node to create an email
subject, an email body. All of these
have variables once again, and I didn't
even tell it to do this. And then we
have our message. And then it uses those
variables to send off that Gmail or
Slack alert. And then you can also see
over here, our AI agent has a system
prompt right here, and its HTTP request
is already set up to Google the company
URL. So that was a real quick demo.
We're going to be diving deeper into
this today with a few different
examples. I'm going to talk about what
works well, how to approach using this,
and also talk about some of the
limitations. So, let's get into it.
Okay, so this is what everyone's been
waiting for, which is Naden's native
natural language to workflow or agent
builder. And I'm super excited about
this because it's going to really cut
down the time that it takes to spin up a
PC. And I think that's really exciting,
but I also am a little bit worried about
the the false sense of security that
it's going to bring to a lot of people.
I think there's no problem in using this
kind of technology. I'm definitely going
to leverage it as well. But what's going
to be really important is after you get
your initial workflow generated that you
can dive into it and understand why it's
doing what it's doing and how to fix it,
how to improve it, stuff like that. Kind
of the same idea of you would never just
download a template and push it into
production or sell it to a client. You
need to understand how it's working
fundamentally. And so quick disclaimer,
if you're opening up your NDN and you
don't see this GPT- like interface,
don't worry. It hasn't been officially
rolled out to everyone yet. So anyways,
now that that's out of the way, what
we're going to do today is talk about
how this works and how to make your
results better. So what I'm going to do
today is three examples. One prompt
that's pretty vague, one prompt that is
detailed and specific with text stack
and stuff like that, and then we're
going to do one that's kind of like a
really agentic sub agent type of system,
and we'll just break down like what
worked well and what didn't. All right,
so this first one that we're going to
do, like I said, super vague, which is
create an AI agent that researches news
every morning and emails me a
newsletter. So super vague. So what's
going on right now is NIDN's AI builder
is thinking about what we want and it's
going to search through NAD's
documentation, the nodes and stuff like
that in order to figure out how to build
this workflow. So what happens is it
comes back and says, okay, based on what
you said, this is what I think you want.
Here are the different nodes we're going
to use. And then at this point, we can
either approve it or we can request
changes. So right now, I'm just going to
approve it and I want to see how well it
does with a very vague prompt. And if
you guys have ever played around with
Enidance AI assistant on the right hand
side, which is what you typically would
use like if you want to troubleshoot a
little bit, this is similar. It's just a
builder rather than just an assistant.
But anyways, you can see what's going on
is it put the nodes in the workflow and
then it connected them. And now what I
think it's doing is making sure that all
of the variables actually flow from one
node to the next. And we'll take a look
once this finishes up, which it looks
like it is done. So it gives us a setup
guide, which is basically saying, hey,
here's what's left to configure. And
then it tells us how to actually use the
workflow. So, let's dive in real quick
to see what it did. So, the workflow
starts with our scheduled trigger
because I believe I said every morning
we want this to go off. Yep, we said
every morning. So, it goes off at 7 a.m.
After that trigger goes off, we have
some workflow configuration right here,
which is apparently just setting the
email recipient, which right now is
unknown. The news topics, which
defaulted to technology, AI, and
business, and then newsletter style,
which is professional summary with key
highlights. So, all that looks good. we
would obviously have to configure our
own email recipient right here. But
let's move on to the next node which was
our news research agent. So if I click
into this guy, what we can do is we can
look at the user message as well as the
system message. And another cool thing
in this update once you guys have it is
that you can open these up on the side
if you want to just look at it full
screen. So the user message for this
agent is to research today's most
important news and create a personalized
newsletter for blank, which is the email
recipient. Honestly, that's not super
crucial because it doesn't really matter
the email address it's going to. It
should just be a good newsletter. But
it's cool to see that it's able to use
context and pass variables through.
Anyways, it then said to include five to
seven top stories with brief summaries
and source links make it engaging and
informative. So, one note here,
typically with these sort of system
instructions, that's really defining the
behavior of the agent. I would throw in
the system message rather than the user
message. Meaning, I would put that down
here rather than up here. That's not a
huge deal, but just a little thing I
noticed. But let's check out the actual
system prompt, which is setting the role
and the behavior of the agent itself. It
says, "You are a news research
assistant. Research the latest news on
topics specified by the user and create
a well- formatted newsletter. Use the
web research tool to gather current
information from multiple sources. Focus
on these topics passes over a variable.
Format the newsletter in blank, which is
the newsletter style." And then it says,
"With clear headlines, summaries, and
source links." So, we've noticed that it
says use the web research tool to gather
information. That's nice. Let's see
what's actually going on down below in
this tool. Okay. So, you can see the
tool is not configured at all. It's
still a default get request. There's no
endpoint. There's no description. So,
the tool isn't configured. We would have
to do that ourselves here. Not too bad,
especially for a first pass. And then
what I'm assuming is the output of this
agent gets passed over as a variable in
the body of the email. As you can see
right here, we have the HTML and the
text. And then we do get a daily news
newsletter which is today's date and
it's formatted nicely. So that's a nice
touch as well. Cool. So that is just to
show you guys a very very vague prompt.
It works but then there's going to be a
lot more to configure. So what we're
going to do is we're going to go back.
We're going to build this exact same
system again but I'm going to give it a
much more detailed prompt and we'll
compare how much better it is. All
right. So I'm back with a more detailed
prompt and I'm going to shoot it off. I
said to build an AI newsletter workflow
that runs every morning at 7 a.m. and it
should use Tavi and Perplexity to
research the top five trending AI and
tech news stories. Then I said pass the
results into Enthropic Cloud 3.5 Sonnet
to write a well ststructured email in
HTML format. So what I wanted to do here
was tell it exactly what chat model I
wanted to use and the different tools
that I wanted to use. So it didn't just
try to throw together a random HTTP
request. I said the email should include
a clear title. It should have subheaders
for every story. It should use bold
text. a short two to three sentence
summary for each story. And at the
bottom of the email, we should have a
sources section with the links to all of
the articles that it gathered. So once
again, it searched through the nodes, it
got all the details, and now it has a
bit of a different workflow structure
for us. And we can go ahead and request
changes if we want, but for now, we're
going to test out the oneshot prompting.
And I'm just going to go ahead and
approve this plan. And one thing that's
interesting is Tavi does have a
community node, but maybe in this
instance, I haven't installed it or
maybe because it's searching through NIN
sort of node documentation, maybe it
wasn't able to find that and pull it.
But either way, we'll see how it's able
to set up these requests. Okay, there we
go. So once again, it's done and it gave
us a setup guide. So we'd have to
configure our email address. We'd have
to get Tableau API key, Perplexity,
Enthropic, Gmail, stuff like that. But
let's go take a look at what it did with
the workflow configuration. So once
again, it did a set node up front to set
the email, the newsletter title, Tavly
API key, and the research query. Okay,
interesting. So what it would be doing
is it would have us put our Tavly API
key here. And then because this is an
HTTP request, it would basically just
pass it over somewhere in here. Okay,
right here. It would pass it over in the
actual body request of the HTTP to Tavi.
So, that is pretty cool though because
based on what I'm looking at, this does
look correct. Meaning, if we gave it a
research query and our API key, this
would work. So, I'm actually just going
to go ahead and do that real quick and
throw my Tableau API key in right there.
And then we're going to take a look at
Perplexi real quick. I've connected my
Perplexi account, so we should be good
there. And then our user message is
actually going to be fixed. So, every
day it's just going to search for find
the top five trending AI tech news
stories from today. So, that's all
configured. It's obviously a lot easier
for any to configure native nodes rather
than an HTTP request, but you can see
right here it was able to do a good job
with Tavi. We've got a merge node which
is appending those results. And then we
have our newsletter writer agent. We'll
take a look at how this thing was
prompted. Okay, so first let's look at
the user message which is saying create
an HTML newsletter from the following
research data. And then here is where
we'd get all the research data. And then
once again it's giving us the format
requirements up here. I would personally
put this in the system prompt instead,
but still let's read through it real
quick. It's telling it to start with
headers and then here would be the
newsletter title. We would have header
two tags. We would have strong bold
tags. We would have two to three
sentence summaries. So everything that I
wanted in the initial prompt that I gave
to this agent builder, it has put into
the prompt. And then real quick, let's
check out the actual system prompt,
which is pretty short. I would have
expected this to be a little more
detailed. It says, "You are an expert
newsletter writer specializing in AI and
tech news. Your task is to create a
well- formatted HTML newsletter from
research data." So then after it's able
to write that newsletter, it would go
ahead and send it to the email recipient
that we set earlier. It would have a
subject of the newsletter title plus
today's date and then it would send it
over as HTML formatting. So now that we
have no more red, I've connected all my
credentials. I'm going to go ahead and
fill in the email that it's going to
send this to and then we'll just give it
a run and see how it works. All right,
so I'm going to go ahead and run this
and it's going to do research with Tavi
Perplexity and then once that agent
writes the final newsletter, we'll go to
my Gmail and we'll check it out. Okay,
interesting. So, we already found one
thing that's going wrong, which is it
appended the data rather than merging it
all back together. So, this output two
items, which basically means we got two
different newsletter copies. So, that's
something we'll have to change. But,
real quick, let's go check in to see how
they actually turned out. Okay, so
here's the first one. And at first
glance, it looks great. We've got a
title, we've got different sources.
Maybe we want a little more meat in each
of these sections, but we do have our
sources section down below. And if we
clicked into them, we could see that
they are real links that take us to
articles. And that is how the newsletter
was actually created. So that's the
first one. This is probably the one that
was Tavly's research. And now let's go
look at the perplexity one. Wow. So this
one looks cool. Um, this one was
formatted differently. So that's the
importance of like being specific on how
you want the HTML formatting because it
looks a lot different. And then we've
got our sources down below of course
again, which we can click on and it
takes us to real articles. This one
actually pulled from a YouTube video
which is kind of cool. And we've also
got a TechCrunch AI article. So that's
kind of how the output was coming out.
So, like I said, there's a few things
that we'd want to change about this. And
what I'm curious to see is if we're able
to use the AI builder to actually make
changes. So, at the bottom, it says,
"Let me know if you'd like to adjust
anything." Okay, so I gave it some
constructive criticism. And what's cool
is it's thinking and it's working. So,
we may see this in real time actually
make these changes. Okay, so we got a
little error. Not sure what's going on
there, but it looks like it is still
working. But anyways, what's going on
here is I said the data wasn't merged
correctly. So, I told it we should want
one newsletter for both sources. And
then I wanted it to change the
newsletter prompt to actually have
inline citations as well, not just a
sources section at the bottom. So we
want to know where each sentence is
actually coming from explicitly. All
right, so it looks like it fixed it. It
found the issue, which was the merge
node was set to append rather than sort
of like combining stuff. And now it told
us here are the changes that we made and
how it works. So what I'm going to do is
just go ahead and save. I'm going to
execute the workflow and we'll just see
if it did actually make those changes
the way we wanted. and then we'll take a
look at the final copy. And actually,
just because I'm curious, while it's
doing this, I do want to see what it
added into the system prompt of the AI
agent or into the user prompt. Okay, so
it added it into the user prompt. And it
said to include inline citations using
one, two, etc. after each sentence. So
that's basically the one change it made
to the prompt. And now we'll take a
look. And once again, it didn't actually
change that merge node. Even though it
knew it was wrong, it still has it
appending. So it's still going to create
two different sources. So, this is why
it's really, really important to still
understand what workflows are doing
because not only is having that
knowledge going to make it easier for
you to prompt this AI builder on what to
build, but it's going to actually help
you troubleshoot and figure out, okay,
maybe I'll just have to manually go
change this real quick and then leverage
AI to help me more with the prompting or
with other variables to be passed over.
But anyways, real quick, let's just go
see if those inline sources came out.
All right. Okay, cool. So, we do have
numbers like this is number one. This is
number five. And you can see what they
relate to down here. It would have been
nice if it would have made these
clickable as well. But because I didn't
give it super super detailed
instructions, it didn't do that. Once
again, the importance here is all the
context that you feed into these AI
assistants to help you do your job
better and faster. But obviously, this
system works. All I had to do was put in
my email address and my API keys. And
then we are getting a newsletter that's
formatted, which this was a lot quicker
than if I would have built all this
myself and prompted everything myself.
All right. And so for the last test that
we're going to do today, I'm just going
to try to throw a pretty complex system
at it. So I'm basically having it build
a personal assistant. I said I want to
interact with it through Telegram. I
want to have a Gmail sub agent, a
calendar sub agent, a ClickUp sub agent,
and then it should use the Think Tool
and use Cloud Sonnet 3.7. So, I'm
interested to see how this is able to
use an orchestrator agent with sub
agents and sub tools and what it's going
to do with those system prompts because
in my mind, this type of AI agent
builder natively in NDN is going to be
really really good at workflows and
passing over variables, but it may
struggle a little bit more with like
autonomous systems. So, let's let it
surf through the nodes and then we'll
take a look. All right, so here it is.
It's not great because we asked for like
you know our Gmail sub aent and our
calendar sub aent to have all of the
tools not just one which here it's just
send message and here is just create an
event and ClickUp is just create a task.
But let's take a look at how it's
actually configured. So what's what's
nice is that at least it labels
everything and it tells you like how to
use it and what you need to set up.
We're getting our Telegram input and
then we're setting the system prompt and
the user preferences. And I'm assuming
that those will feed right into the
agent itself. But if we actually click
into the agent, I already see one big
issue, which is that it's looking for
the connected chat trigger node for the
input, which is not actually here right
now. Anyways, from here, the system
message is basically saying you are a
sophisticated personal assistant that
helps users manage their digital life.
You have access to specialized sub aents
for Gmail, Google, and ClickUp. And you
can use the think tool to analyze
requests. Always provide helpful
professional responses. So it may have
just got confused with Telegram because
I don't know if the chat ID and
everything is configured correctly as
well or the memory itself. But let's
take a look at these sub agents and see
how they're working. So we've got our
user message which is the Gmail request.
Up here it says you are a specialized
agent for Gmail operations. And down
here it says you are a Gmail specialist.
Handle all email operations efficiently
even though it only has one tool. And
then I'm assuming that the other sub
aents are set up the same way. This one
actually doesn't even have a user
message. So this agent would error right
away. And same thing with the ClickUp
sub agent. There's no user message here.
The easiest way to fix that is we would
literally just click on this button and
the agent itself would be able to get a
message from the main agent. But
anyways, this kind of proved the point
that I was trying to make, which is you
probably don't want to use this to build
complex multi- aent autonomous systems.
Because if I go back into this workflow
builder, and you can see there's some
pre-built ones right here, right? Let's
just say we're looking at an invoice
processing pipeline. A system like this
is going to be a workflow, meaning we're
going to go from step one to step two,
from step two to step three, and we're
going to be able to keep this AI
workflow on the path that we set for it
very clearly. These types of workflows
are a lot more predictable. They're a
lot easier for a human to build, which
means it's going to be a lot easier for
an AI system to build. And you can see
that these prompts are specific. It's
telling the workflow exactly what to
extract, what type of information. It's
telling it where to store the
information in Air Table and when to
actually trigger this AI analysis and
what to do with it. So, I guess as we
sort of start to wrap up here, what I'm
trying to say is that this is super
super cool. I think it's going to be
great to leverage this to help you with
your prompting, setting up your HTTP
requests, getting a really good idea of
where to start. But what you need to do
in order to actually make that efficient
is you need to know very clearly what
you want to build. And you need to
understand how AI workflows actually
work fundamentally because the more
context you can feed in and the more
information you have about like where
does my data come from, what do I want
to do with the data, what fields am I
looking for, what type of AI analysis am
I going to do? All of that information
is going to make your job a lot easier.
And that's going to be super valuable
because I'm aware of like sometimes you
get in here and you don't know which
nodes to use or where to start. And this
type of technology will give you a
really really good place to start and
from there you can customize it and
learn what's going on. So you guys can
see with a workflow like this, I'm
assuming that all of these variables are
going to be pretty much perfectly mapped
over. We have an AI agent that's given a
user prompt in order to parse the
information, but look at this guys.
There's actually no live variables being
fed into the agent. So this agent knows
it needs to parse an invoice, but it's
not actually receiving any invoice
information. So you still want to be
able to look at this and identify that
all of this is fixed and there's no live
variables being passed through. But then
from there we have a validation check
which is just making sure that this
stuff exists that it's not empty
everything like that. And if it's true
we're going to store the invoice data in
air table which will be the variables
that we were looking for earlier.
Anyways I just wanted to show you guys
how this AI builder works. My honest
thoughts on what it's going to be good
at and what it needs to improve on a
little bit. And more importantly, I
think the mindset of leveraging this
stuff should be the same mindset that
you have when it comes to AI automation
in general, which is AI is not going to
do everything. But if it can get me 70%
of the way there or cut down 70% of my
time from it, that is a huge win. And
that's the way you should be thinking
about approaching this as a tool that
you can leverage to learn quicker and
build quicker. And speaking of learning
and building quicker, if that's
something you're interested in, then
definitely check out my plus community.
The link for that is down in the
description. We got a great community of
members who are learning and building
with Naden every single day. And we also
have a straight path to starting to
monetize your AI automation and spinning
up a oneperson AI agency. You can learn
here as a beginner, move into the
foundations of Naden, and then start to
figure out how you can actually start to
work with clients and build a business
off of this. So, if that sounds like
something that you're interested in,
then definitely check it out. The link
for that is down in the description. But
that's going to do it for today. If you
guys enjoyed the video or learned
something new, please give it a like.
Definitely helps me out a ton. And as
always, I appreciate you guys making it
to the end of the video. I'll see you on
the next one. Thanks everyone.
Full courses + unlimited support: https://www.skool.com/ai-automation-society-plus/about All my FREE resources: https://www.skool.com/ai-automation-society/about Have us build agents for you: https://truehorizon.ai/ 14 day FREE n8n trial: https://n8n.partnerlinks.io/22crlu8afq5r In this video, I dive into n8n’s new Text-to-Workflow builder and show you why it’s such a game-changer for saving time. I run through three live examples so you can see what kinds of prompts work well, where it struggles, and how to think about using it the right way. I also cover the limitations and the mindset you need: this tool is a great starting point to cut down build time, but you’ll still need to understand the fundamentals of how workflows work if you want to make them reliable and improve on what the builder gives you. #n8nAIWorkflowBuilder Sponsorship Inquiries: 📧 sponsorships@nateherk.com TIMESTAMPS 00:00 Quick Demo 01:39 What We’re Covering Today 02:57 Test #1 06:34 Test #2 13:29 Test #3 15:52 Bonus Test / Final Thoughts 18:18 Want to Build a Business with n8n? Gear I Used: Camera: Razer Kiyo Pro Microphone: Blue Yeti USB