---
title: 'n8n Masterclass: Build AI Agents & Automate Workflows (Beginner to Pro)'
source: 'https://youtube.com/watch?v=ZHH3sr234zY'
video_id: 'ZHH3sr234zY'
date: 2026-06-15
duration_sec: 0
---

# n8n Masterclass: Build AI Agents & Automate Workflows (Beginner to Pro)

> Source: [n8n Masterclass: Build AI Agents & Automate Workflows (Beginner to Pro)](https://youtube.com/watch?v=ZHH3sr234zY)

## Summary

This masterclass teaches n8n, a low-code/no-code automation tool, from beginner to advanced AI agent builder. It covers setting up n8n, building workflows, creating AI agents with RAG and vector databases, using APIs, and best practices for scalable automations.

### Key Points

- **What is n8n?** [00:30] — n8n is a low-code/no-code automation tool that allows users to automate processes and build workflows with minimal coding knowledge using a drag-and-drop interface.
- **Benefits of Workflow Automation** [01:52] — Automation increases efficiency, reduces human error, saves time and money, enables scalability, improves data handling, and enhances customer experience.
- **Why Learn n8n?** [02:51] — n8n empowers non-developers, offers over 300 built-in integrations, and allows connection to almost any tool via API or webhook.
- **Self-Hosted vs Cloud** [05:10] — Self-hosted offers control, data ownership, and cost-effectiveness but requires technical maintenance. Cloud is easier, managed by n8n, and subscription-based.
- **Workflows, Nodes, Executions** [08:28] — Workflows are recipes, nodes are steps/ingredients, and executions are each time a workflow runs. Analogy: restaurant recipes, ingredients, and orders.
- **Four Main Node Types** [16:34] — Trigger nodes start workflows, action nodes perform tasks, data transformation nodes process data, and logic nodes make decisions.
- **Building a Customer Order Workflow** [19:00] — A Google Sheets trigger detects new rows, OpenAI summarizes the data, and Gmail sends the summary. Demonstrates connecting nodes and testing steps.
- **What is RAG?** [30:56] — Retrieval-Augmented Generation combines retrieval from external sources (e.g., vector databases) with generation by AI models to provide accurate, up-to-date answers.
- **Vector Databases Explained** [38:33] — Vector databases store data as vectors (numerical representations) to enable semantic search, finding related information even if exact words differ.
- **Embedding Data into Vector Store** [40:24] — Process: load data, split text (e.g., recursive character text splitter), embed using OpenAI model, and store in Pinecone. Demonstrated with a Nike earnings PDF.
- **Building an AI Agent with RAG** [51:08] — An AI agent uses a chat trigger, OpenAI chat model, memory, and a vector store tool to answer questions about Nike's earnings from the PDF.
- **Custom Tools and Scaling Agents** [62:32] — Agents can use custom workflows as tools, enabling reuse and combination. Agents can also call other agents for hierarchical task delegation.
- **APIs and HTTP Requests** [71:34] — APIs allow software to communicate. HTTP requests (GET, POST) are the messengers. n8n's HTTP Request node can connect to any API with proper documentation.
- **Error Workflows and Best Practices** [82:25] — Error workflows trigger on failures, sending notifications. Best practices: organize workflows, use sub-workflows for reusability, implement error handling, and optimize for scalability.

### Conclusion

This masterclass equips you with the knowledge to build from simple automations to complex AI agents in n8n. Start building, experiment, and join the community to continue learning.

## Transcript

all right today we've got a very
exciting one this is the nadn master
class where ideally I'm taking you from
a beginner in nadn all the way to an AI
agent Builder by the end of this or even
just someone who wants to implement AI
automations into their daily life or
into their work so I was about to say
grab a pen and a piece of paper but more
realistically since you're here grab
some sort of AI notetaker and let's dive
into this one this is a master class so
we're going to start at the bottom start
with the basics and we'll continuously
work our way up but just want to start
off here with what is any
so at this point I'm sure you guys have
been hearing the term low code no code
tools and nadn is a low code no code
automation tool so that just basically
means that nadn allows users to automate
processes build workflows with minimal
coding knowledge and the key idea behind
this is that low code means it's very
easy to develop things with a very
userfriendly interface where people can
just go in there and drag and drop
different components different nodes is
what they're called in nadn to create
these flows without having to come in
here and type a bunch of JavaScript or
python this is so significant because
it's going to allow anyone to be able to
get an in and get up and running with
building automations even if you don't
have you know a background in computer
science or programming the barrier entry
to this is so low it's very very
accessible for anyone to get in here and
start playing around with stuff but even
though it's very simple it also retains
a lot of flexibility for more advanced
users who do have coding background and
they're able to come in here and take
some of the basic principles and also
come come on top of that with you know
custom code or different type of logic
and Integrations and it's a very very
powerful tool you can build tools
directly in NAD as you see here and
that's a little bit different from other
things like make or zappier because you
can build let's say an agent that's able
to call four or five tools and these
tools you built within NN itself which
is just super super cool stuff what
about the importance of automating workf
flows so we've got five points here I'll
touch on real quick increasing
efficiency and productivity automation
is going to eliminate repetitive tasks
it's going to reduce human error and
it's going to allow you and your team to
focus on higher value work it's also
going to save time and money of course
automating workflows is going to reduce
operational risk free up Time by
completing tasks faster than manual
scalability and adaptability you can
scale a lot more effortlessly you can
focus on your growth as a business and
these Solutions these automation
Solutions can be customized or adjusted
to meet your changing needs you've also
got improved data handling automation is
going to integrate data from various
sources it's going to provide real-time
insights for better decision making and
then the last benefit I wanted to hit on
here was enhanced customer experience
you're going to be able to respond
faster to your clients or automatically
to your clients personalize interactions
through automated workflows and all of
this is just going to lead to better
customer satisfaction and Customer
Loyalty moving on here we've got why
should you learn nadn so when I was
building the slide I had so many
thoughts and I tried to put them all
under three main bullets and so this
first first one here is nadn empowers
non-developers with Automation and I
know we've touched on this a little bit
with the whole low code no code stuff
but it's just so powerful that anyone
can pretty much come into NN and start
building things in 15 20 minutes that's
actually going to automate real work
that they would do on a daily basis so
even if you're not a programmer you can
come in here create workflows it's going
to save you time make your life easier
for example you could very easily get up
a workflow that is going to
automatically move data from one app to
another like ually copying contacts from
one spreadsheet to another without
having to do it every time it's like
having a digital assistant that can
handle these repetitive tasks for you
and you don't really need the technical
skills to set it up so super cool stuff
the second one we've got access to over
300 built-in Integrations which is
insane andn comes with a ton of
Integrations ton of connections to
popular tools that you probably use
every day like Gmail um Google Sheets
slack Twitter we got Microsoft stuff too
if you want to connect to teams or
Outlook it's super super cool what you
can do these Integrations let you
connect quickly to these tools and you
can connect tools to other tools without
needing to like code in between that for
example you could set up a workflow
where every time you receive an email
it's going to automatically add that
information to some sort of spreadsheet
and then it's going to send you a
notification on slack or teams and all
of that will take place without you
needing to be in there and doing it
manually then this last one you can
connect to almost any tool so kind of
similar to the second point but if
there's something that you want want to
connect to that there's not a built-in
integration to you can still pretty much
connect to it whether that's through an
API or a web hook um these ones are a
little more technical but for the most
part um you know a quick YouTube video
or using chat gbt even you'll be able to
get them up and running and connect to
almost anything you want a little bit of
custom code and um really when you
realize you can connect to almost
anything then you have almost endless
possibilities of what you want to
automate all right we're going to be
moving into part one of this master
class here which is just very simple
getting started with NN we're going to
talk about how you set it up the
different ways you can set it up and
then we'll just get into the interface
and start actually learning what it
looks like and what everything does the
first thing I think that we should talk
about when it comes to setting up your
NN is if you want to set it up
self-hosted or if you want to Cloud host
and I'm going to run through a few
features of each of these two different
options and then we'll talk about which
one you should choose so first with
self-hosted some of the things that it's
going to offer you are control and
flexibility you're going to have full
control over your environment you can
customize your server integrate with any
internal system you have the ability to
adjust your configurations as needed
we've got data ownership so all the data
and workflows are going to stay on your
private server so this is ideal if you
need to comply with privacy regulations
or you want to keep sensitive data
in-house cost self-hosting can be more
cost effective in the long run it
depends on the server and maintenance
costs but there's no ongoing
subscription fee to naden like if you
were to do Cloud hosted but with the
cost as aspect you may need to account
for infrastructure with like database
management or server hosting and you
also may need to have some sort of team
that will help you maintain it but who
knows fourth with self-hosting is
installation and maintenance you are
going to be responsible for setting up
and managing and updating your instance
this also includes backups and scaling
so this is going to require more
technical knowledge then finally we've
got customization so you can modify the
source code you can add custom features
that might not be available in the cloud
environment so this is cool if you want
complete freedom to modify the system as
you want and now moving on to the cloud
environment here are some features of
cloud it's going to be easier to use
because it's managed by nadn itself so
there's no need to worry about setup
updates scaling maintenance stuff like
that so good for beginners you've also
got availability and reliability so the
NN cloud is hosted on a scalable and
reliable infrastructure it's going to be
maintained by Ann's team so they're
always going to keep stuff up to date
with the latest features and Bug fixes
the security is a little different it's
going to be managed security so SSL
certificates or you know secure API
handling and that's going to be done by
the N team and then also this is going
to be more suitable for users who don't
want to handle server security now when
it comes to cost of nadn it's really not
that bad either way the cloud version
comes with a subscription model that's
going to be based on usage tiers how
many products you want how many seats
you want on your you know an inn
environment and so you'll be basically
paying based on that so in the long run
it could be more expensive if you have a
ton of users and a ton of projects going
on but if that's the case then hopefully
you're either saving a ton of money or
you're making a ton of money so balance
is out then finally we have data
handling different than self-hosted data
is going to be stored and processed in
the cloud so like I said if you're a
business dealing with highly sensitive
data this may be a limitation due to the
server dependencies okay so at this
point you may already have an idea of
which one you're going to choose but if
you don't real quick you should go with
self-hosted if you need full control
over your data and your infrastructure
if you want to integrate any in deeply
with other on premise systems and if
you're technically comfortable handling
server maintenance and management or you
have a dedicated team that will do that
for you and then you're going to want to
go with Cloud if you prefer Simplicity
and you don't want to handle
infrastructure or server maintenance you
want a quick setup and reliable hosting
it's going to be managed by the NN team
and you're okay with paying for a
subscription for a managed service and
you don't mind being handled by a third
party provider okay so before we
actually hop into NN and we look around
at the interface probably important to
understand the difference between
workflows nodes and executions so I'm
going to break this down as simple as I
can pretend you're in a restaurant we've
got workflows which are going to be the
recipes nodes are going to be the
ingredients the steps within each recipe
and then executions are going to be
every time someone sits down and orders
that specific recipe or workflow so the
workflow think of it as like a set of
instructions that you're going to be
giving to nadn in order to automate a
task so for this example we'll say that
the workflow is a a chocolate cake then
we'll move down to nodes nodes are like
the building blocks of the workflow each
node is going to represent a single step
a single action within a workflow so one
node might send an email one might
update a spreadsheet one might pull data
and then you can kind of Link those
together in order to make the chocolate
cake so you know eggs flour baking soda
we're going to put those together then
we have the execution which is simply
just running your workflow in nadn this
can happen by different triggers whether
you want to do that manually or whether
you want the automation to take place
every time you update a row in a
spreadsheet but in the example like I
said just picture someone coming to the
restaurant and ordering a piece of
chocolate cake and then you kind of
start that process of making the cake
and delivering the cake all right now
that we've covered the three main
building blocks that go into pretty much
every automation let's actually get into
nadn look around a little bit at the
user interface see what I'm talking
about when I say drag and drop we'll
talk about accessing Community Resources
Community templates stuff like that and
it'll really start to make more sense
all right so we're in nadn as you can
see as what we're looking at right now
is we're just on my homepage so it's
going to show me a ton of the different
workflows that I've been working on um
we've got our projects right here so I
only have the one right now it's called
Nate testing so in here it's pretty much
just everything I have but if you had a
specific project for a specific client
or a specific project for an actual
specific project at work you could add
them here so you can keep everything
organized honestly my stuff is not too
organized but here's what it looks like
we've got stuff on the Le hand side we
can see we have an admin panel we have
templates we have variables we can see
executions of each workflow we've got
some help and then you have your profile
down here as long as well as you know
I'm Cloud hosted so I can see that
there's some updates here with some bug
fixes that I'll I'm going to be able to
just go in and install real quick let's
just add a new workflow right here and
see what the interface sort of looks
like so this is the canvas that I was
talking about where I said it was a very
userfriendly drag and drop interface the
first thing you're going to see is that
you have and add First Step button so
we'll get into different types of nodes
after this and we'll talk about triggers
and all that but first step is always
going to be a trigger so you'll click on
that and it will list up you know some
triggers trigger manually just means
that you'll be hitting test workflow
button down here in order to run the
workflow execute the workflow every
single time you can schedule it you can
do um on chat message you can call it by
another workflow so that's where all the
stuff is super powerful we'll just add a
manual one so you guys can see what it
looks like this is where you hit test
workflow in order to run it and you know
obviously nothing's coming through but
it didn't fail so that's good then from
here you would want to add different
nodes to connect to so you could do that
from either clicking on this plus button
where it says click to add node or drag
to connect or up in the top right you
can click up here and it will pull up
this panel for you to search through
nodes you know by category or you can
just search for them if you want um
another cool thing with the triggers is
there's not just those triggers there's
different triggers for each app so let's
say that you wanted to run a workflow
every time you got an email you can
click on on Gmail down at the bottom
you'd see different triggers and this
one says on message received so this
would execute the workflow every time
you get an email and you can tell a
trigger if it has this little lightning
bolt so that's a trigger node but
anyways let's just say that we wanted to
put some fake data in here so I would
just come in here and I could either
type for the the node that I want or I
know I'm looking for an edit Fields node
so I can come to data transformation and
I would see edit Fields right here this
is where I could configure stuff so let
me just quickly pretend um we're going
to make make a field called name and
we'll just put in my name and what's
really cool about NN is that you can
test each step individually as you're
going through and automating something
so rather than having to run the whole
workflow you could just test step right
here we'll see that what's coming out of
this node is Nate um in the field called
name so we have that information running
through but it's nice to know because
you'll always see on the left of this
configuration panel you'll see data
that's coming in and then you'll see
data that's coming out so it makes it
really easy to troubleshoot and test
each step individually which is really
cool
but you have to make sure that your your
um nodes are connected because let's say
you know I didn't drag this one right
here and connect it to the edit Fields
if I was to run
this nothing would come through the edit
Fields there's no output as you can see
it says wire me up this node can only
receive input data if you connect it to
another node and yeah so that's how
that's going to work I say this is
probably all we'll do for now in here
we'll get into some Community templates
and just show you how that all works but
as far as just basic setting up a
workflow and seeing what um the
interface looks like how easy it is to
just drag and drop stuff that's what
we've got so back in the homepage we've
got workflows you all can also look at
your credentials so this is just
different things that you've connected
to like I said there's so many
Integrations so it can easily access my
Google Drive my telegram my Google
Sheets all that kind of stuff so that's
where you can sort of see and manage
your credentials and then down here the
only thing I'll touch on real quick will
be the templates you can click into
templates and it will pull up nad's
website where there's sort of like this
community where people can upload cool
things they're building
or you can search for specific use cases
specific um you know tools that you want
to use so it's a really great place to
get in here and learn but as you can see
we have you know learn by doing so you
can download these templates which is
really cool sometimes it's you know it's
nice to watch a tutorial on YouTube but
being able to really learn you have to
just get in there and you have to let
things fail in order to figure out why
they're failing so right here we have
you know AI agent chat we can download
from NN we can click here we can look at
it um and we can see what's going on we
could click this button here to download
it and um start playing around with it
in our own nadn we have this one where
you can click in here and a lot of times
people will annotate like what's going
on so you can see how to set it up you
can see which you know what's taking
place in each scenario so that's super
super useful and then I'll just show you
real quick let say you wanted to use
this one we could just import the
template to my cloud
environment and as that loads up it's
pretty much just going to put it right
into my workspace so I've got this
information here I can now test this
data I can look at what's going through
each step we can see right here we've
got um you know this information's
coming through and then it comes out as
you know flour eggs milk which is
interesting because I just did a you
know a little example about cake so um
seems like it's meant to be but either
way you can start to see actual data
moving through which is how you're
really going to be able to wrap your
head around what's going on another
great thing about nnn is there's so much
documentation it's super easy to get
help so if you come down here you can
see help we've got a lot of stuff here
they even have a course that you can go
through through but documentation if you
click on here you can see pretty much
anything you need to find like there
there's quick starts there is um
Concepts about flow logic Concepts about
data so like I said super easy to learn
about all this kind of stuff you can
look at you know what each node is doing
specifically so let's say you were
confused about the loop node you could
come in here read about
looping you could see you know how the
node works you could maybe see some
examples of how people are using it and
yeah like at the bottom it's probably
going to throw you in some you know
templates of actual workflows with loops
but super easy to get help Within naden
part two of the master class we're going
to be talking about some Core Concepts
so we just saw what the interface looked
like we saw a few nodes but now we
actually need to dive into different
types of nodes and what they do and then
we're going to end this section of the
master class with an actual example
where we'll get into nadn I'll do a live
build of a really quick Automation and
then we'll talk about the different type
of nodes and how data is moving through
so it should be pretty cool to see so
before we in nadn and we build out our
first automation we really need to
understand these sort of four main types
of nodes so we've got Trigger action
data transformation and logic so let's
just break these down real quick
starting off here with trigger nodes
because a trigger node is pretty much
going to be what starts every workflow
we just saw these in nadn they were the
ones with the little lightning bolt next
to them anyways different types of
trigger nodes we can look at something
like a web hook trigger um an email
trigger like I showed you anything
that's going to start the the workflow
whether that's going to be manual or on
a chat or on an event or I have called
by another workflow bolded here because
it's sort of the power of NN you can
build a workflow that will be called by
another workflow and then you can build
an agent that can call that workflow as
well as maybe this agent can call like
10 other workflows so super powerful
stuff here next we've got action nodes
these are the actual doers they're going
to perform a very specific task within
your workflow so it's like an assembly
line this guy is going to to um you know
put the present in the Box this guy's
going to wrap the present this guy's
going to put the bow on the present all
that sort of stuff but they can do
different things like you know send
email create a record make an API
request they can get a text message they
can set your calendar um almost anything
that you could do on your computer on
your phone manually you could have some
sort of action node to do this thing
third we have data transformation nodes
these are going to help you change or
process your data in some way so that it
flows through the whole process and you
get the end result as you want want it
so these type of nodes can do things
like set that can add Fields change
values within Fields it can do some sort
of processing your data you've got
something like an aggregate where you
can combine a ton of data into a single
output or something like a merge where
you can combine data from two different
sources and put them into one the last
type I'm going to touch on real quick
are logic nodes these are sort of the
decision makers they're going to help n
and figure out what path to take how to
handle a different situation so we've
got something like an if node it's going
to check if a specific condition is true
or false is you know this value higher
than 10 it's going to go this way
otherwise it's going to go this way
we've got a switch node which is going
to allow you to put multiple conditions
in there and it's going to be checked
and direct the workflow to a specific
action based on that conditional check
and then something like a weit node
which is going to pause the workflow
let's say you wanted to pause the
workflow until you come back and respond
like yes that's good to go then it will
continue to move on through the rest of
the process or you could have it like
wait for 20 seconds um what whatever you
want it to do all right now it's time to
finally hop back into NAD in hopefully
these slides weren't too boring but
we're going to be building an example
workflow here it's going to be really
simple it's just going to automatically
process customer orders and then it's
going to summarize it and send us a
report automatically every time we get a
new order so super cool stuff let's hop
into how we're going to build this thing
all right we are in a Google sheet this
is going to be the customer order data
that we'll be using for this example so
I had chat gbt make up some data for us
we've got stuff like order ID customer
name product quantity price order date
and the status of that order so every
time a new row is put into this Google
sheet it's going to run through nadn
automatically it's going to get
summarized by some sort of large
language model like chat gbt or Claude
and then that summarization is going to
be emailed off back to us or back to our
team automatically so this will be a
nice simple example but it's going to
feature different types of nodes and
we'll be able to see the data move
through real time so it'll give us a
really good base we are now in n8n this
is the canvas we'll be working on and
this is the workflow that we'll be
building here um NN masterclass customer
orders so we know the first step that we
always need to do is add some sort of
trigger we'll click in here and we can
see different triggers like manual um on
app event called by another workflow we
talked about this but in this case we
want to make this one automatic so we're
going to be doing a Google Sheets
trigger Google Sheets has three triggers
it's got on row added on row updated or
on row added or updated so we'll do this
one because that way if you have to go
in there and change some sort of
information about an order like let's
say one goes from you know the status is
pending to shipped or something we'll
also get an email about that so we have
to set up the Google Sheets account um
this is where you're going to have to
set up a credential and I'll walk you
guys through how to do this so you're
going to click create new credential and
you'll see this screen pop up where we
need to grab a client ID and a client
secret um this looks a little confusing
and I was definitely confused when I
first saw the screen so what we need to
do is right here um I talked about how
nadn has is really good at having
documentation that explains stuff so
we'll click on open docs right here we
will see that the prerequisite we need
is a Google Cloud account so we'll go in
here and make a Google Cloud account um
and then we can see that it's going to
walk you through step by step so if my
explanation isn't good enough you can
come in here and grab the docs but
hopefully I'll show you real quick how
to do this so we'll come into Google
Cloud um this is what it's going to look
like you'll have to sign in make an
account then you want to go to your
console once you're in your console
you'll see the screen
you might not know what to look at but
all we want to do is we're going to
create a project so mine right here is
just called my first project make sure
you're in your project and then you want
to come in this left hand side to apis
and services we're going to click on
enabled apis and services and at this
point you just need to search for a
Google Sheets so we're going to type in
Google um okay we need to do sheets be
more specific so Google Sheets we can
see Google Sheets API we'll click in
here and then all you need to do is just
enable this API so we've got ours
enabled already
um there will be a button right here
it's just as simple as that so get that
enabled and then you're going to come in
here go back to your apis and services
and then we want to go to
credentials once we're in credentials um
this is where you can set up your client
IDs to get an ID and a secret so I'll
just walk you guys through how we're
going to do this one you're going to
click create credentials up here you'll
go to ooth client
ID once that loads up you want to choose
the application type this is going to be
a web app you can name it whatever you
want we'll call this one demo for for
the sake of this video and then all you
need to do here is add a redirect URI so
this is where you see in nadn you've got
this UD redirect URL um we're just going
to click here to copy go back into
Google cloud and we're going to add this
right in here just just paste it in and
then you'll hit create you'll get the
screen pop up with um your client ID and
your client secret so it's as simple as
copying the ID pasting that into the ID
field in NN going back to Cloud grabbing
your secret and pasting it into the
secret then you want to sign in with
Google so this screen's going to pop up
it's just going to be a simple prompt to
sign in with Google like you would
normally have um so I'll drag this in
right here and now it says that Google
hasn't verified this app so this is
where you need to set up your ooth
consent screen um so back in here you've
got your ID and your secret now you need
to go back in your credentials right
here ooth consent screen all you need to
do here is either make sure that your
app is published so um you need to make
make sure the app is published so that
it has you know access to actually go
through and grab information out of your
Google Sheets your drive your email
whatever it is or you can add yourself
as a test user so I've got my emails
down here as test users this also will
allow these emails to sign in and go
through but if you're getting blocked
for some reason it's probably because
you didn't set this up right so just
make sure it's published or that you're
in there as a test
user so back into nadn um we've got this
signin field we'll hit continue just
make sure you give this email access to
everything give NAD access and then
you'll hit continue and then pretty much
you'll be good to go you'll see we got
this account created right here it's
green we're good so we will come out and
now we're
connected so now that we're connected we
can configure the rest of this node it's
going to be running every minute and
that's when it's going to be checking
for if a row was added or updated now we
can select the document that we want so
it's really nice you can choose from a
list you can enter the URL or the ID of
your document but list is so much easier
it's just going to access your Google
Drive and see what sheets you've got so
we're going to do customer orders um
there's only one sheet in this document
so we'll grab that sheet and it's going
to trigger on row added or updated so if
we can fetch a test event here we'll see
some of our sample data coming through
so as you can see we've got um five
items we've got the columns up here and
then we have all of these orders that we
just had right here in Google Sheets so
we've got John Jane Mike Emily Robert
Brown and in here you can see we've got
John Jane Mike Emily and Robert Brown so
this node's working we've got our
information coming through into NN now
we want to add an open AI node so we'll
just click on this plus right here or
click on the plus up in this top right
corner and you can search for a new node
we're going to grab open AI you could
use a different large language model if
you wanted to but I'm going to be using
open AI you can see we've got 15
different actions within this node what
we want to do here is message a model
basically just means that we're going to
be talking to chat GPT just call this no
summarize and now we need to hook up
this node with our credentials so at
this point if you don't have an open AI
account you need to do so and then once
you do that you can come in here click
create new credential this time all we
need is a single API key so once you
have your open AI account you'll come
into it on left hand side you'll see all
the stuff but we just want to go to API
Keys up in the top right you can click
create new key give it a name and then
it will give you a value to copy so
pretty simple same thing you just want
to come in here and copy that
information in or sorry past that
information in hit save and it will go
green once you're all good um so that is
all you got to do but pretty much every
time that you need to configure a node
you're going to have to grab some sort
of key so just keep that in mind so as
you can see the resource is text the
operation is messaging a model and now
we need to choose what model we want to
message so I'm going to come in here and
grab GPT 40 right there and now we need
to configure the rest of this node so
this is the message that we're sending
to GPT 40 you have a couple options here
you can have it be a user message an
assistant message or system message you
can see the differen is right here
usually when you're going to be
prompting the node how to act we're
going to choose system so in here I'm
going to type a quick prompt and I'll be
right back to explain it all right here
is the system prompt that I came up with
just typed this out really quick so I
said you are in charge of client orders
your job is to take incoming information
regarding new orders and give a nice
summary that will be emailed to the team
the email should be signed off from
customer success team and then we want
to give it the information from the
previous Google Sheets trigger that's
coming in here on the left we need to
actually give it the information to
summarize so it's going to be getting
order ID customer name product quantity
price order date and status and so all
we need to do is come in here and drag
and drop each of the
fields into the prompt and it will be a
variable so it will change for each one
so I'll just show you guys order ID we
drag this in and I don't know why it
does that um we want to make sure that
right here with order ID so first of all
first thing to note this is a green
variable with the two curly brackets
around it that just means that it's a
JavaScript variable so this doesn't
involve any coding it's as simple as
dragging and dropping but this just
means that it's going to change based on
whatever value is in this field so as
you can see in the result tab right here
we've got the the json. order ID is
coming through as 101 because that's the
first order and you can see similar
things when we drag in customer name
we'll get in the the results we got John
Doe um we'll drag in everything else and
then I'll show you guys so we're just
going to drag in product quantity price
order date and Status so that's assigned
wherever it needs to be and then as you
can see in the result we've got here's
the information on client orders we've
got the correct order ID name price
quantity all this kind of stuff we have
the actual information coming through as
you can see and then the last thing that
we wanted to say was please output the
following parameters So based on this
information that it's getting right here
that it's going to sum summarize it's
going to Output an email subject for us
and an email body for us so we can go
ahead here and hit test step and I'll
show you one thing about how we want to
Output this content as Json so I didn't
check this yet and so that means that
this is going to execute and it's going
to all come out in one sort of like
large string so as you can see we've got
um for the first order email subject new
order confirmation order ID 101 here
actually let me make this a little
bigger so it says hello team we have a
new order that has been successfully
processed and shipped below are the
details so it's going to summarize out
for us and then it signs off thank you
for your attention best regards customer
success team but this is coming through
as one big chunk of text called content
so what we want to do is output the
content as Json we'll test the step
again and now it's going to come through
as two separate Fields one will be the
email subject and then one will be the
email body and this is just important so
that we can drag and drop the next
Fields later when we want to configure
the actual Gmail node so as you can see
right here we've got the subject and
then we've got the body and we've got
the subject and the body we'll read one
more real quick so order 103 we've got
the subject and then the body says we
have a new order summary order ID 103
Mike Johnson you got headphones at $200
just one pair please let us know if you
need further details so as you can see
these are all coming through as an email
subject and an email body and this will
be important when we set up this next
node here which is going to be a Gmail
node this time we're going to add the
node by clicking the plus up here it's
the same thing as this plus but just
wanted to show you guys different ways
we're going to grab a Gmail node and
there's 25 actions within Gmail as you
can see there's a lot of stuff you can
do which is just awesome but here we're
going to be sending a message so we'll
click on send a
message um real quick let's just make
sure we wire this one up otherwise it's
not going to work and then we'll come
back into the node and we can configure
it so first thing you got to do is
obviously set up your new credential
you'll come in here just got to grab
that same client ID and secret from the
previous one so we'll come back into um
our enabled apis and services
you want to go to
credentials and then you can click into
that um client ID that we just made and
then all you got to do once again is
copy and paste that information in so
let me just do this real quick we'll
grab the
secret paste that in there and then once
again just sign in with
Gmail it's again going to make you
verify the app we'll go through give
access to everything and then we go
green because we're good to go so we've
got that set up now all we need to do is
configure the rest of this node so as
you can see the resource is a message
the operation is that we're sending a
message now you can see why it's so
important that we set up the email
subject and the email body as two
separate Fields because we output it as
Json so all we got to do is drag in this
subject right here where it says subject
so the subject of this Gmail being sent
off will be order confirmation 101 for
John Doe and then same thing with body
you're going to grab that and put that
in the message field where this is the
actual message that's going to be sent
in the email we we want to make the
email type is text that's just how I
usually like to do it and then for the
sake of this example we will just put in
my email so we can see this email coming
through and you could make this variable
you could make this change based on the
order if you wanted to but right now
let's keep it simple we're just going to
send it to nerk 88@gmail.com every time
and finally you've got some options here
you could attach things you could CC
people you could change the sender name
variably whatever you want to do here I
usually just will come in here and click
append and add an attribution and make
sure I turn that off otherwise at the
bottom of the email it'll just say this
email was generated by nadn or sent by
nadn so this looks good to go we can
test this step and it will it should be
firing off five emails because we have
five emails um in the sample data so you
can see these came through it's just
giving us a message ID and a thread ID
which right now we don't need but you
can see that all of these got sent so
let's hop over to the email I will
refresh and we should see five new
emails in my inbox so yeah we've got
order um 101 102 103 104 and 105 so this
is information from all the orders as
you can see this one says new order from
Robert Brown he got a tablet three of
them actually 600 per unit so the total
price was 1,800 it was on August 28th
and the status is still pending so thank
you best regards customer success team
so all that came through exactly how we
wanted it so that's good to
see now we're back in n8n and we're
pretty much done with this workflow
we've got three nodes in here and we can
save it and now what we want to do is
check this box to inactive or sorry to
active so all this means is that now
that the workflow is activated it will
regularly check Google Sheets for events
and then it will trigger executions each
time that um a row is added or
updated and they won't show immediately
in the editor so that means like we
won't be seeing like these turn green
every time it actually is going through
but they will'll be going through so
let's hop into the Google Sheets real
quick add a new row and then let's let's
wait for the email okay so I've got this
information I'm about to paste into here
we've got order 106 for Phil dumpy he
ordered 500 crayons $5 for each one
we've got the date and we got the status
so now we're just going to hop into our
email and I'll just refresh and we
should see the new email coming
through okay so I just refreshed and we
can see this new New Order we've
received the New Order from Phil dumpy
here are the details 106 Phil dumpy 500
crayons $5 per unit we've got the date
and then we have the status so um as you
can see the date is actually coming
through is January 20
2025 um and we did not put January 2025
so what you can do here is because
there's you know a discrepancy we will
go back and N ITN we will go to our
executions over here or right here so
executions we can see this is the most
recent execution we'll click into here
and we can see what's actually taking
place
so as you can see the trigger went off
it grabbed one new item we'll open up
this message and model node so we can
see the information coming through and
you can see the date come through here
as
45585 so that's not what we want um
that's why it's coming through as um
January 20 2025 so what we need to do is
come in here and fix this specific node
because we see exactly where the issue
is happening I think the issue here was
just weird formatting with numbers
coming through as far as dates so let's
just try this one again I just out of
this row so it should be working right
now to fire off this email to us but we
kept this one as just plain text so
hopefully it reads through an n and end
as plain text but let's take a look at
the email and then we'll take a look at
the actual execution okay just refreshed
got this new order it's coming through
order 106 and we got the date correctly
as October 20th 2024 which is what we
put right here so let's go back into Ed
and let's refresh this page so we can
get the most recent execution and making
sure all the data is coming through
exactly how we want it and it's a great
thing to keep in mind that you're able
to go into the executions you can see
exactly how stuff's coming through so
from the Google Sheets trigger we're
getting this information and the date's
now coming through correctly how we want
it previously it was coming through as
just like 4554 whatever sometimes in
sheets it can be weird with date
formatting and then it's being able to
come through correctly order date and
it's giving us a nice summarization
right here we've received New Order with
the following details order ID 106 for
Phil dumpy crayons 500 all this kind of
stuff and then it's just going to take
that subject and and um body and put it
into a Gmail node which is being sent
over to Nate herk 88@gmail.com
which is what we see right here so that
was it for this first example I know it
was very simple we just utilized three
nodes to build this workflow that
automatically takes data every time a
row is added in your Google Sheets it's
going to summarize it with a large
language model of chat gbt 40 and then
it's going to move into the Gmail node
which actually sends it off and this was
one execution of this work worklow okay
now we're moving into part three of this
master class which is going to be
talking about Rag and Vector databases
I'm sure you've heard these terms but
maybe you don't completely understand
them so I'm here to break it down for
you and then we're going to end this
part with going back into nadn building
out a simple rag AI agent and this will
include you know actually uploading
information into a vector database and
then being able to use an agent and rag
in order to go talk to that PDF or file
and get answers back okay so what is is
rag rag stands for retrieval augmented
generation and it's a very powerful
technique that's going to combine two
different approaches the first part is
retrieval and then the second part is
Generation so this technique really
helps AI models provide accurate and
relevant answers especially when you
need upto-date or specialized
information so the first part here is
retrieval when you ask the AI a question
instead of it making up answers based on
its training data it's going to retrieve
relevant information from um external
sources so in this case the pine cone
Vector database that we're going to be
setting up um so this is obviously going
to be the database but it could be other
documents or it could be websites and
then the generation aspect of it is
after it retrieves back this information
that's relevant and accurate and up to
date then the AI model will use this
information to generate an answer so
this is where the AI actually crafts a
human readable response with the
information if you don't already
understand from that previous slide why
rag matters let's break it down real
quick let's say for this example you're
using an AI assistant that needs to
answer questions about your company's
internal policies you don't want this
thing to just guess um answers based on
its training data it might be out of
date because you're going to update
policies stuff like that so in this case
the AI assistant will use rag to
retrieve the most relevant information
from the system it's going to generate
an answer based on that specific
information which makes the AI far more
reliable and up toate for your needs
okay rag is a pretty simple concept to
wrap your head around now we're going to
move into databases which I think are a
little more complicated but once you
really break them down not that bad so
in order to make rag work the system
needs a way to store and retrieve data
efficiently this is where the vector
databases are going to come into play so
in simple terms Vector databases store
data in the form of vectors which are
just numbers that represent the meanings
of words or text or whatever it is and
it's going to store these vectors into a
multidimens dimensional database so
these vectors are going to help us find
find similar or related information much
quicker than sort of like a relational
um structured database this one's going
to be using a lot more unstructured data
so even if the words are not the exact
same for example you're asking about
cars the vector database might also help
you find related information about
vehicles or automobiles as you can see
in this picture down here we've got um
wolf dog cat and then the query is a
kitten so it's going to be searching for
a kitten information about kittens and
it will kind of be um in this
three-dimensional
database store it'll be seeing like
similarities based on characteristics of
these things and as you can see like
we've got fruits over here and then you
might have vehicles down here and you
might have like you know information
related to certain types of products up
here so that's kind of how this works
just as like from a visual perspective
so if you're wondering how Vector
databases work in relation to rag the AI
is going to convert documents or text
into Vector stores and it's going to put
them in the vector database where they
need to be then when a question is asked
the system is going going to look for
similar vectors and sort of search out
the right area to pull information back
um you know relevant documents or data
and then once it finds these most
relevant vectors it's going to retrieve
that information and then finally it's
going to generate an answer for the
human once you understand what a vector
database is what Vector stores are you
need to understand how can you actually
get information into a vector store so
this next slide is going to talk about
sort of embeddings and stuff like that
all right so embedding data into a
vector database this this slide is going
to be kind of tailored towards doing
this in n8n there's other ways to but NN
is the way I do it so real quick let's
just break down this picture so what's
going on here is we're testing the
workflow it's going to be searching a
Google Drive for a specific file it's
going to pull that file and then we need
to embed it into the pine cone Vector
Store Pine Cone is just a vector store
database that we use um it just seems to
be you know very cheap very easy to use
so this is the one we're using but
there's other Vector databases out there
you might have heard of something like
super base but this one's really simple
then what it's going to do is it needs
to load the information so the type of
information coming through whether it's
Json or binary it's going to load that
it needs to be able to split it up you
know it's going to chunk it up and then
it's going to use the open AI model to
embed it into the actual Vector store so
I know that this stuff may not make
sense yet we'll get into an actual
example in NN where we're building this
out and we're getting real PDFs from our
Google Drive up into pine cone and we'll
see all that take place but we just
wanted to give you a quick visual real
quick quick so you can understand what's
going through the first thing I'll touch
on real quick um is the default data
loader aspect of this so in NN when we
connect the vector store we have to load
the data this node is basically just
going to allow us to load data from a
previous step flowing through um you
know right here and then we need to load
it into here so that we can actually
chunk it up and get it embedded into the
vector store so this note is pretty much
just going to be looking at what kind of
data we're loading in um if it's like
Json or if it's binary that sort of
stuff and then we can just you know
configure how much we want to pass
through stuff like that and then we move
into actually text splitting so right
here I have a recursive character text
splitter as you can see right here the
three options in NN will be character
recursive character or by tokens so the
first option is character text splitter
this is just going to split the text
into chunks based on a set number of
characters so you might want to use this
when you want to break down text into
equally sized pieces regardless of where
sentences or paragraphs end um and then
the next one we have is recursive
character text splitter which I seem to
use the most because this one is going
to split text by characters but it does
so intelligently because it's going to
break down logical points like after a
sentence or in between paragraphs stuff
like that but same concept of just
chunking stuff down so this is
recommended when you want to keep the
text meaningful instead of cutting off a
sentence in the middle it's going to
split at natural breaks like after you
know a period or a comma something like
that and then finally the token splitter
this is going to split text based on
tokens which are usually words or
subwords that the model understands and
you kind of want to use this when you're
working directly with a language model
like Chachi BT because it's going to
process text in terms of tokens and you
know it's going to chunk It Down based
on how the AI model reads the text so
you know if you're processing data for a
model it's going to split the text
accordingly hopefully I didn't confuse
you guys too much maybe I went into too
much detail but just wanted to break
down those different types of splitting
but best practic is a lot of times you
can just use recursive character so the
information stays meaningful but um just
a quick summary here so the rag is going
to be um retrieving information from
documents right here that we're putting
into a vector store in order to give us
intelligent answers then the vector
database is going to store text in a way
that allows us to quickly and
efficiently search based on meaning not
just exact words that are you know
hardcoded in we're looking at stuff
that's related to specific meanings of
words and then we're going to use a text
splitter to um help break down the large
documents into manageable pieces in
order to put them into to the vector
database and then open I AI here in this
case is just going to embed it into the
vector store so that is basically how
it's going to work and we'll hop into NN
and actually show you guys this as you
can see what we're going to do here is
build an RG rag AI agent and in this
workflow we'll be using a Nike earnings
PDF and we're going to put that into
pine cone which is the vector database
that we'll be using and then we can chat
with the agent in order for it to
retrieve information about Nike earning
so that we don't have to read through
the PDF we you can just ask questions
about it all right like I said we're
going to be looking at this PDF of Nike
earnings reports so we're going to see
this is the PDF that we're looking for
it's 10 pages we don't want to really
have to read this thing we want to be
able to just chat with an agent and it
can pull the information for us so first
up here get some sort of document that
you want to put into pine cone Vector
store then as you can see I have this in
my Google Drive right here so that we're
able to actually you know call this
information and push it into pine cone
through nn and then you want to go into
pine cone it's just type in Pine cone.
it's free to get started you want to set
one up you'll come into here and all you
want to do is you're going to create an
index so you can name it whatever you
want um I'm pretty much just going to
keep everything as is the only thing
that you want to make sure that you set
up here is right here you want to set it
up by model and you want to choose text
embedding three small so you'll set that
configuration and then you'll just hit
create index your index is going to pop
up right here if you click into it you
can see that there's no information you
can see that there's no name spaces in
here um just a really quick explanation
of name spaces you can have different
name spaces within each index like let's
say I was in here and I had one for um
internal documents and then I had one
for um client a and then I had one for
client B that's just going to help your
agent be able to search for the
information quicker because it can sort
of break it down by okay I need to go to
this index and I need to go to this
namespace and then here's all relevant
information regarding this project or
client once you've got all that
information set up we are good to hop
into to NN and start pushing information
into that pine cone Vector store so the
first thing that we're going to do here
is I'm just going to make this a manual
trigger in the future you could have
this where every time you upload a
document to a certain drive it would do
a Google Drive trigger and then it would
automatically push that information into
pine cone which is super cool because
then your database is going to you know
stay up to date every single day every
single time you add more information but
right now we're just going to be doing a
manual trigger next thing we need to do
is add a Google Drive node because we
need to get that information from Google
Drive drive into NN so we're going to
click on this plus button going to type
in Google and we will see Google drive
right here once again lots of actions
the Integrations are awesome but what
we're going to do here is download a
file so if you haven't got this node
configured yet it should be super easy
because you've already set up your
consent screen and your client ID and
secret and all that but in here you just
need to go and make sure that you have
enabled the right API so as you can see
here I've got Google Sheets Google Drive
Gmail Google Docs custom search all the
different apis that I can use within in
NN so set that up make sure you're
connected to the right account and then
we're going to be downloading is the
operation and we're grabbing a file the
resource and once again we can choose
from a list which is awesome we're going
to come in here and look for the Nike
press release PDF so I'll grab that and
then what we're going to do is just hit
test step because then we can see the
information coming
back so on the output you can see that
we're getting this information one thing
that's really important to know is that
it's not coming through as Json there's
no information here it's coming through
as binary so we don't have to get too
technical on what exactly that means but
we have to just make sure we know how
this information is coming through so
that later when we want to embed it into
the vector store we can make sure it's
getting loaded correctly and I'll I'll
show you guys that later but for now
just remember that this PDF is coming
through as binary so we can view this
PDF make sure it's the right one as you
can see it's Nike earnings so we're good
to go here and we can move on to the
next step we've got our file next we
need to add the actual pine cone Vector
store so so we can push the information
into pine cone so we're going to add
this we've got four actions within pine
cone right now we're just going to be
adding documents to a vector store but
as you can see you could also retrieve
you could update all that sort of stuff
and we will be retrieving later in order
to actually chat with our agent about
this PDF once again it's a little
Annoying to have to set up the
credentials for everything but once you
have them you're good to go so in here
we need to set up the pine cone I'm
going to create new credential and as
you can see we need to grab an API key
once you hop back into pine cone you can
see obvious you've got your indexes on
the left hand side you can go down to
API keys and then all you're going to do
is just copy this value with this button
right here copy that and then you're
just going to really quickly paste that
into the API key hit save and it should
go green because our connection is
successful now we're actually able to
insert documents to the index so the
index that we want to insert to is
called sample and for the sake of this
video let's add it to a namespace so
click on ADD option Pine code namespace
and we will just call this one
Nike and now we're good to go with this
node but what we need to do is we need
to set up like I talked about earlier
the the default document loader how
we're going to check Chunk Up the text
and then the actual embedding so first
we will do the embedding I'm going to
use open AI you you should have your
credential already set up and then we
need to choose the model so if you
remember when we set up our pine cone
index we set up the model of text
embedding three small so we don't want
to do Ada O2 we want to come in here and
grab three small so that it's being
being embedded
properly then we need to choose this
plus button and we're going to do the
default data loader like we mentioned
now here is what I talked about with we
need to remember how the information is
coming through this Google Drive so
remember we came in here we see the
output is binary not Json so binary is
how we want the information to come
through so in the data loader we need to
make sure we're selecting the type of
data is going to be binary otherwise
you're probably not going to get any
information put into pine cone so we're
good to go here the last step is just to
set up the text splitter so like I
talked about the differences between
these three as you can see there's also
a short little description here so if
you forget you can always come in here
and read what they do but we're going to
choose recursive character text splitter
the chunk size like we talked about is
just how many characters are going to be
within each chunk and the overlap um we
don't want to have any overlap and chunk
size 1 th I'm sure that's fine for now
the PDF is pretty big but we can see how
this works so let's just hit save and
then we will test out this workflow so
it's a manual trigger so we're going to
hit test workflow it's going to grab the
file download the PDF as you can see it
went through the data loader it went in
here and then it had to embed it until
it came into the actual Vector store so
let's hop back over to Pine Cone let's
go to our database let's go to the index
called sample we can see that we have
information in here now and if we click
on names spaces we will see that we just
created a name space called Nike and
there are 29 vectors in here and as you
can see 29 items left the pine cone
Vector store node all right our
information has been successfully put
from Google Drive into pine cone now we
need to build an agent workflow that
we'll be able to chat with in order to
get answers from this PDF all right
we're in a new workflow here and once
again we got to add a trigger so the
first step is going to be a chat message
because we want to talk to the agent in
order for the workflow to start
execution so we'll click on chat message
we can leave this as is because we'll be
using this button down here to actually
you know talk to the agent and that
that's how it's going to work but we
have our chat message trigger now we're
going to add a new node we can come in
here to Advanced Ai and we see there's a
ton of different AI things we can do um
there's even some templates up here to
see you know what's possible and you can
download those and start playing with
them but we're just going to come in
here and grab an AI
agent now within this AI agent you have
different types of Agents you can use
you've got tool conversational or openi
functions agent you can read a little
bit about what each of these do but
because we're giving these agent
different tools I think that we'll just
keep this one as a tools agent come in
here and call this guy our Nike agent
and then you can also do things like add
a system message you can return
immediate steps you can have him have a
max amount of iterations we will add a
system message right now it's just going
to say you're helpful assistant we can
set this up in a sec once we get all the
tools configured but this is where we
sort of tell the agent you know this is
your job here's background information
here are the tools that you have here's
how you use them here's like an example
flow so we'll talk about all that after
we get the rest of this workflow
configured we've got our Nike agent and
then you can see there's different
things we need to set up so the first
one is going to be the chat model I'm
going to grab an open AI chat model and
connect our credential once again and
then we choose the type of model we want
since this is going to be pretty
conversational I think I'm going to use
40 it just seems to be the most
consistent it's kind of the one that I'm
pretty loyal to but sometimes for
smaller things like if you're just
labeling emails or if you're um doing
some sort of classifier where you just
need to parse the information and see um
like a category maybe then you could
come in here and grab you know 35 or 40
mini but don't get too caught up on what
each model's good at but right now 40 is
kind of the most expensive but it is the
most
powerful so we set that up with 40 now
let's really quickly add a memory so
this is super super easy we just want to
grab a window buffer memory um this is
super easy because that's all we have to
do we don't have to set anything up you
could change the context window length
but five chats is how many the model is
going to remember so I'm fine with that
but this is a really easy way to give
the agent some context of what's going
on otherwise when you're chatting with
it let's say you asked um what was
Nike's earnings in you know quarter 3
and then if it came back with the
information and then you said okay what
about quarter 4 it would be like what
are you talking about quarter 4 for what
so that's going to give context of oh he
just asked about earnings now he wants
to know about quarter the next quarter
so just just going to give context to
your agent super easy way to add that
memory then finally this is where all
the magic happens this is where you can
add different tools So within here we
have you know different things that we
can give our agent access to of course
we've looked at all the different nodes
and the actions they can take but we can
see here that this is where it's really
powerful because we can call an NN tool
or an NN workflow as a tool so that
workfl that we just made about um you
know getting information into pine cone
we could call as a tool here but that's
not exactly what we're going to do we
are just going to call um sorry a vector
store
tool and this is the one that's going to
be getting our um Nike information so we
will just call this um database and then
you need to give it a description of
when to use this tool so we'll say call
this tool to um read to get get
information about Nike's
earnings to answer the user
question okay so that is the description
for this tool now we need to set this up
with the actual Vector store because we
need to connect this to Pine Cone and
then a model of course so let's add the
model real quick pretty much same exact
thing we're just connecting the
credential we're adding a model I'll
just do foral mini here um and then we
can see we need to grab the vector store
so we have in memory we have different
options here super base that I talked
about but this is how we actually want
to work with data within our pine cone
Vector store so we're going to click on
that
um I'm going to set this up real quick
and now we see there's different
operations this is the time we want to
actually retrieve documents we don't
want to put anything in there right now
we're just trying to get information so
we'll click retrieve we need to choose
the index that we want which is just
sample and then here's another option
where we can add the name space for this
agent to go search through so we called
ours Nike so make sure you put the right
name space in here and make sure it's
spelled correctly too so now we have
that set
up so we're almost done with this agent
last thing we need to do here is ADD the
embedding which once again we did um
three small so we need to set up three
small once again okay so this is pretty
much it for this agent we should be able
to talk to it and have a conversation
with it now so let's hit save and let's
just give it a shot so real quick let's
go to the PDF and ask about something so
we can see the gross margin for the
fourth quarter increased 110 basis
points to
44.7% so let's ask about um the gross
margin for the fourth quarter so we'll
come back into nnn we'll chat with his
agent um we'll just say how was
Nike's um gross margin for
the fourth quarter see what he
says Nike's gross margin for the fourth
quarter was
44.7% so that was right that's the
information we're getting but we maybe
don't like the way that this agent's
talking to us so that's where we need to
actually come back into the agent and
prompt it so let me just type out a real
real simple prompt and then we will take
another look all right I went into chat
gbt and I said hey can you help me
prompt this agent it needs to understand
its role some context instructions I
want to give it some example flows of
how it should operate and we got a
pretty good prompt out of it so let's
just read through it real quick we said
you are a friendly and helpful Nike
representative tasked with answering any
questions users may have about Nike's
earnings you have access to a vector
database with all the relevant data on
Nike's financial performance including
Revenue profits other earning related
info when a user asks a question you
should search this database to find the
most accurate and up-to-date information
and respond in a friendly approachable
tone be sure to add humor and use emojis
to make the conversation fun and
engaging then we gave it instructions
for an interaction flow so basically we
said a user asks a question you're going
to search the database you're going to
respond and then we gave it some
information or some examples of a
friendly tone greeting the user throwing
emojis using jokes um all that kind of
stuff and then we wanted to give it a
sample flow sort of like more exact um
the more examples that you can give an
agent about you know what it might run
into different situations the better and
then finally we said the actual tools
that it has so Vector database is really
the only tool we hooked up and we said
to use this to retrieve specific
earnings information and financial
performance remember your goal is to
provide accurate data while keeping the
user engaged with humor emojis and a
conversational tone all right so let's
give this a save and ask another
question let's let's just come in here
and say um who is Matthew friend because
he's the Executive Vice President and
CFO of Nike so we'll say who is Matthew
friend and like what did he say I guess
let's see what we get from
that who is Matthew friend and what are
his
thoughts okay so here's what we got from
the agent Matthew friend is the
executive VP and CFO of Nike he's the
financial Mastro and ensuring the Swit
stays profitable and Innovative and then
we have um two emojis there let's see he
said he G the agent gives us the quote
that he said and then at the end it says
if you have any more specific aspects
you're curious about I can dig up his
latest commentary for you so good emojis
very friendly um let's just say sure can
we get some more
info and this is information this is
important because it's going to remember
what we were just talking about which
was Matthew friend and we can see if
there's anything else in this PDF that
he said um so here's a slice of wisdom
from Matthew friend the CFO he recently
highlighted that while Nike is driving a
better balance across his portfolio the
fourth quarter brought some challenges
but no worries he's on it Matthew
emphasized that Nike is taking strategic
actions to reposition itself for
sustainable profitable long-term growth
okay so we're seeing a conversation with
this agent right here in the log you can
see exactly what's happening so you can
see our agent um it updated the memory
it went to the chat model it read
through it prompt and then it basically
is like making sure it knows what to do
it's going to go to the vector store
tool and we have the query which is
Matthew's friend Matthew friend's recent
statements or comments and then it got
an output from the um pine cone database
so if you don't understand what's going
on here basically it's just being able
to see the flow of what's going on so
that you can um you know troubleshoot if
need be but this is super cool and it
just shows you how you can connect
different tools so we could even come in
here and add a um what is it Wikipedia
so this is going to let it search in
Wikipedia so if there's information
maybe that's not about um that's not on
this PDF it could also access this tool
and we'd have to obviously prompt it a
little bit to do that but let's just see
if this is going to work we can say what
is the capital of Florida and it should
be searching through wikkipedia to
answer that question so the capital
Florida is Tallahassee it's not just
about beaches and theme parks haah nice
and friendly but um you know this
information the capital of Florida I
doubt that it was on this earnings
report from Nike so that just shows you
that it actually went and searched
through this tool and you know you can
also add like a calculator in case you
want to make sure it's doing you know
math
accurately um so we got a calculator
tool here now too so we would prompt in
that but it's super cool because like I
said you can connect different workflows
that you build within NS tools so let's
say we have this agent here and we give
it a tool that um can send emails
because we're going to build a workflow
of automatically sending an email and
and then we would just give the agent
this this tool so that if we wanted to
chat with the agent and say hey by the
way could you send this information to
um you know Matthew friend in an email
and it would actually be able to go to
do that as long as it had Matthew
friend's email which we would give it in
some sort of vector store as well so um
I hope that that you know is a breaks
down the concept of rag Vector databases
um pine cone Vector store how you can
link all these together when it comes to
giving an agent access to all these
different things in order to do what you
wanted to so at the end of that last
build we saw we started expanding on
that agent and giving it access to you
know Wikipedia and a calculator tool and
so I wanted to talk a little bit more
about how you can actually expand on
these agents to make them even more
powerful and scalable um you know like
giving an agent access to more tools
giving an agent access to agents to call
on it's it it's super powerful the stuff
you can do so in this part just wanted
to quickly talk about building workflows
as tools how that all works how that all
comes together and the importance of it
and then we'll just go through a couple
examples in NN of some agents that I've
built and you can just see the way that
they use different tools all right the
power of being able to build custom
Tools in NN it's it's honestly insane so
first we have the fact that agents can
use these tools obviously you can build
a tool and have an agent call on it like
in this example down here you can see um
I have get email tool send email tool
update database summarize database set
calendar event and get calendar all of
these are tools that I built within nadn
so these are workflows I'll show you
guys them once we hop back into nadn but
these are all different tasks that I
built out in nadn and then the agent is
able to decide based on what I tell it
texting it on telegram based on what I
tell it to do it will decide which tool
to use in order to go complete that task
and then it will either tell me that it
did the task or it will give me you know
the summary of a database or my calendar
that sort of stuff so agents can use
these tools um you know like a smart
agent or a smart AI assistant that can
call these workflows so this is a great
example right here of a personal agent
we also have the fact that tools can be
reused and recombined so now that I have
these tools built out I have a send
email tool if I ever need to build a
different type of agent to send emails I
can just give it this tool I've already
built it so it's already there in my
workflow and I can call on it in
multiple different agents it won't
really matter so that is super cool they
can be reused anytime and they can be
combined with other tools and now for
scaling so this is what I talk about the
fact that as you build out tools you
just have more and more tasks that you
can complete um more things that you can
give your agent to do and then it gets
even more powerful because um let's say
you want to not just have a send email
tool but you want to have an agent that
can do everything within email so you
would have an agent and you would give
it a ton of different tasks and email so
youd have an agent with get emails send
emails label emails draft emails delete
emails all this kind of stuff and then
you could give your overarching like
larger agent access to the agent that
does email stuff so this agent would be
able to decipher okay do I need to go
into Outlook or calendar or teams or
slack and then you would down here have
one agent that does everything in slack
One agent that does everything in teams
One agent that does everything in your
calendar and then you can just build on
top of each other and also that's going
to make your workflows more efficient
rather than trying to you know send a
prompt through with like giving this
agent you know 50 to 60 tools like that
would be way too much even I think like
20 is probably too much but that way you
could give the agent other agents and
it's just like you know the hierarchy of
it's going to go through this guy then
it's going to go to these agents and
then it's going to come back so it's
just you can get really creative here
with how you can get stuff done and all
you have to do is break it down by tasks
so take a task and combine these with a
larger workflow of getting all these
tasks done and then you know larger just
just scale up pretty much so now let's
just hop into nadn and we can take a
look at you know this assistant and a
couple other ones all right so this is
the personal assistant that we were just
kind of taking look at back in the
slides but as you can see it's got just
pretty much seven tools it's got
database information so for something
like getting or sorry sending emails it
needs you know contact data information
who do I actually send it to what's
their email address and then we have
these different tools get emails send
email get calendar set calendar and then
update or summarize the database so real
quick I'll just show you guys how I
talked about these ra all tools within
my nadn so if I go back here we can see
um here's the update database tool
here's the calendar email so we can like
click into one of these so let's do
summarize database as you can see it's
just a very simple workflow we've got
different nodes we've got um the actual
database it's going to call on this
database it's going to summarize it
aggregate everything into one clean
field and then it's going to send the
response of the information back to the
agent so it's going to go through this
process then it's going to have a
summarization right here once we get
that information summarized it's going
to go back to the agent and then it
knows its job is done so then it's going
to Output a telegram message back to me
so real quick let's take a look at this
database this is the project database
that I'm summarizing in this case so
we've got different tools or sorry
different projects we've got notes about
the project and then we have the
different statuses so I know it's a very
simple
example but that's just you know I was
testing out this personal assistant and
trying to make a video about it so
here's the assistant let me just pull up
my telegram with my that I talked to for
my AI assistant um as you can see
there's different information that I've
been testing out with other workflows
and other executions but let's just come
in here and say can you summarize our
database and so this is going through
telegram the agent is under getting this
prompt right here can you summarize our
database it's figuring out which tool it
needs to call in order to do that and
then it's going to summarize the
database as you can see we just got this
message back here's a summary of the
current status and contents the a AI
project is complete the marketing
campaign is pending it involves drafting
content this that is ready and awaiting
review by the marketing
head um mobile app project this project
involves developing a user
authentication module which is currently
ongoing beta testing has been scheduled
indicating progresses in the works so as
you can see it's you know summarizing
all this information for us could be
super useful if you were on the road and
you need to send a quick email so you
just have to text this agent real quick
or you know on your way to a meeting and
you need to summarize get some quick
information summarized it could even
summarize all the emails you've gotten
from a certain day so that is a cool
example of um building an agent and
giving it examp giving it access to
different tools that you've built in NN
by the way if you if you want to know
more about what this agent can do please
go watch the video I'll tag it right
here um I made a whole video about you
know building this personal assistant
and sort of like the capabilities of it
and how you can expand on it so
definitely go watch that video if you
want a more in-depth look at what this
agent does here's another quick example
of a different way you can structure an
agent this one is being triggered by
Gmail so every time I got a new email
it's going to come through here it's
going to classify the email give it a
label of high priority customer support
promotion finance and billing and it
will actually give it a label on Gmail
and then for each of those types of
email it's going to come through here
and select for high priority one it's
going to create a draft and then it's
going to have the draft sitting in our
email and then what I would do is um
actually I made a video about this one
too so if you haven't seen it I'll tag
this one too and you want to you know go
look and see how this works and how to
build it but then what I did in the
video is we hooked it up to a send text
message node right here in telegram so I
configured this and then it was able to
let me know hey we made a high priority
draft for you based on this email from
Kevin and now it's like there for you
and then I did the same thing with all
these other ones so like for customer
support we let it actually create an
email and reply to it so it actually
sends off an email and then the telegram
message says um we sent off an email for
you based on you know it was a customer
support email same thing with these two
down here in you know if I pull up
telegram we can actually see these past
interactions I've had so like for a
finance and billing one down here we
wanted it to summarize the information
and then send it to the finance
department so right here we see you
received a finance and billing inquiry
from Angela from the accounts Department
we've notified your finance department
of this email um right here it's like a
promotional one so here are details
regarding a promotional email from Nate
it gives us a summary of the promotion
and then it gives us a recommendation so
that's something that we crafted out
right here and all of these were linked
up to different telegram nodes to let us
know notify us of what's coming through
and what the agent had done so like I
said go watch that video if you want a
more in-depth run through of what this
um agent does but the purpose of me just
showing you guys this real quick was
just to open your eyes about how you can
expand how you can build off of you know
different ways you can structure agents
and how you can you know make them do
exactly what you want to do and remove
yourself out of that process to automate
things so just super cool stuff moving
on to part five which is going to be
talking about apis and HTTP request
quests um this kind of stuff can sort of
get a little Technical and seem
confusing but I'm here to make sure we
just sort of break it down as simple as
possible so before we really get into
the content of this part I wanted to
just stress that you know we've already
been working with API calls and API
tools whether you've known it or not all
of the preconfigured nodes in nadn are
pretty much just HTTP requests in some
way or another so when you're using
these nodes naden is doing all that hard
work of making the API call for you you
know either fetching or getting some
data like in that previous example when
we were using that Google Drive node to
get information to put it into our pine
cone Vector store that was pretty much
an API call to Google Drive looking in
our Google Drive grabbing the file and
then we got the information back in Ann
so we're pretty much already doing that
you only will really need to use apis
and HTTP requests in nadn if you want to
connect to something that there's not an
integration for which um is kind of rare
but it's good to go over just in case
you do need to do this so yeah the an NN
they know exactly what to do exactly
where to go where to send their requests
and how to get the information you need
or put information somewhere that you
need to so now we can move into talking
about apis so what is an API it stands
for application programming interface so
basically just think of it as the bridge
that is going to allow two different
softwares to talk to each other like
nadn and Google Drive whatever it may be
so here's a concise summary about API
endpoints calls and HTTP requests so so
um the endpoint is just basically going
to be a specific URL or address within
the API where a certain service or piece
of data can be accessed so it's like the
exact path that you need to take once
you access that API you have that
endpoint so it's going to specify where
you need to go then the API call is just
that request that you're making to the
API asking it to you know perform some
sort of task or provide some sort of
data so it's like you're placing an
order and then the HTTP request is the
actual method that you use to send that
API call over on the internet so it's
sort of just the messenger that's going
to carry your request to the API
endpoint and then it's going to bring
the response back in a nutshell you're
going to be making an API call using an
HTTP request which is going to be sent
to a specific API endpoint and then
we'll get the information back from the
API and then the HTTP request is going
to return the information to
us okay so what is an HTTP request think
of this as just the way that your
computer or nadn is going to be talking
to the other service so you can do
things like get data which will be sort
of a g HTTP request or you can send data
which will be a post HTTP request which
you'll see once we hop into NN and
actually look at some examples but just
as simple as either asking for
information or sending information
somewhere I remember when I first heard
about all these different terms I
thought to myself like that sounds so
similar how do you really distinguish so
let's just quickly talk about how they
actually work together so like we said
an HTTP request is is how you actually
make an API call it's the messenger
that's going to carry your API call to
the server so we've got a quick
restaurant analogy let's think about it
like this so we have the API this is
like the restaurant itself so this is
the service that you're talking to the
restaurant is going to provide different
services to its customers you know just
like an API the restaurant offers a menu
of things that you can request different
actions or different data then we have
the API endpoint the API endpoint is
like this specific kitchen station that
you're talking to so it's going to
handle a particular dish there are
different stations for each tasks you
know cooking pasta making pizza so the
end point is like going to the correct
station in order to get the specific
dish that you
ordered then we have API call um an API
call is like placing the order it's the
actual request so in this case you know
we wanted spaghetti that's how we know
to get it to the right spot but um you
know you look you you'd order the
specific meal and then um that's just
making the request um for data or for a
specific service from the API and then
finally we have the HTTP request which
like we said is pretty much just the
mechanism that's being used to deliver
the request so in this analogy it's
going to be a waiter who's going to take
your order bring it to the kitchen staff
and then when they bring the dish the
waiter is going to bring back that food
to bring back that information that you
were looking for so hopefully that was
simple enough API call is the concept
HTTP request is the tool so you're
asking for something with an API call
and then the request is going to be how
you're delivering it over the internet
all right now let's just get into NN
real quick and just look at a few
examples of an HTTP request node and
sort of what it looks like to configure
something like that we are now back in
nadn as you can see I've got three
different HTTP request nodes in here so
you would just come in here and grab
HTTP request as you can see right here
um but these first two we've got two
gets so we'll be asking for information
in one way or another and then this last
one will show a post where we're
actually sending information somewhere
so let's just go into this first example
here
so this one's going to be a really
simple one we're making a get request
like I said so we're asking for
information here would be like sort of
that API endpoint like we talked about
so we're going to be going to
openweathermap.org um we're going to be
asking for weather you can see here's
some parameters Q equals New York so
we're looking for weather from New York
and then we have a little credential
here we had to set up an API key to
actually be able to access um the API of
open weather map and get into you know
that's my API key so it knows that we
have permission
and we'll hit test step here so we can
just see that this request is working we
can see information coming through on
the right hand side we've got clouds
we've got temperatures we've got wind um
and then as you can see it came back for
the name the city of New York so we know
that this request was working I won't go
too much right now into you know setting
up parameters and headers and body for
your request but usually when you want
to connect to a certain API they're
going to have documentation on it so in
this example for open weather map they
have exactly like the end points that
you need to find or they they'll give
you what you need to type in and how to
specify parameters it's not like you
have to know how to just find the stuff
cuz that seems pretty technical um so
yeah most of the things that you want to
connect to if there's not an integration
in NN already we'll have documentation
on their website of how to connect to
different things how to request for
different things how to send different
things so you'll just have to read
through documentation but another cool
thing obviously like we were getting
weather from open weather map but as you
can see like open AI already or sorry
nadn has Integrations for this where
it's a lot easier because this is
basically setting up that API call they
just did all the coding the technical
stuff on the back end of that so we have
this basically the exact same thing okay
so this next one is another get request
this one is as you can see we're going
to be searching Google so we have the
endpoint right here of google.com/
search but we did want to set up some
parameters here so we have q like we saw
that last one Q equals New York this
time Q is equaling site colon
linkedin.com
slin okay so this is the URL that we're
basically trying to access so if we p
ped this into Google right here we would
see like it brings us up Google so
that's what we're searching on and then
within Google we want to be searching
for site umon linkedin.com so we go back
to Google paste that in there and you
can see what's coming back is actual
LinkedIn just LinkedIn profiles so
that's sort of how this parameter is
working and we can go ahead and test the
step real
quick we can see exactly what's coming
back which is going to be a nasty chunk
of HTML a lot of information in here
your next step here would be to parse
through this information with a
different node that would grab you just
LinkedIn profiles so like if I come in
here and search um linkedin.com you can
see like I'm sorry we've got a lot of
hits 173 hits and we can sort of go down
until we find actual profile so that's
what you'd have to be parsing out but
right here we have Robert W Livingston
so if we go back to the actual Google
search we can see that first profile
coming through is Robert
Livin um and then if we were to continue
to go down and look through all the
different results we'd see all these
different profiles coming through into
our naden so that is the way that this
request is asking for information from
site colon linkedin.com in and then
we're actually getting back the
parameters from or sorry the information
from Google through that request and for
this final one we're making a post
request as you can see right here so I
click into this we'll see what's going
on in this node this is a post request
and I was able to go to Google apis in
order to see how to access my calendar
there's going to be different API
endpoints in their documentation of like
you know copy this URL if you want to
create event copy this URL if you want
to update an event copy this URL if you
want to get information back on your
events so that's all I did I I hooked up
that that API endpoint in here obviously
had to set up my credentials and then in
this example we have to send a body
because we're posting data sending
information and so this is really simple
it's just Json um sort of setting up the
criteria for the event you could you
know go into chat GPT and say hey I'm
like accessing a Google API for my
calendar can you help me set up a body
and it should work with you there but in
this case the summary of the event that
it's going to be making is meeting with
team we have a start date um noon we
have an end date of 1:00 and then we can
add like attendees and different emails
and real quick I'll show you this is the
calendar that I'm accessing right here
so there's nothing going on today and
we're going to make the event for noon
so if I hit test step it'll come through
and it'll say that it worked and we can
see on here we just got our meeting with
Team from noon to 1:00 p.m. and the
information coming back here is just
going to be like you know meeting link
it's just going to basically tell you
that that post request went through
successfully all this kind of stuff but
another case where that would just be
over complicating things you've got
Google Calendar right in here um where
is it right here we can get availability
we can create an event so all we did
right here was create an event this is
going to make it a much easier way to
actually send that request because you
just have to fill in different
parameters and you don't have to worry
about the API endpoint putting that in
you don't have to worry about um you
know the Json sending over the data in
that post request you could just do that
right here as well now that that concept
of just how you access endpoints and how
you actually send or receive data makes
more sense what you would want to do
from here is like the more realistic use
case when you're building stuff like
this and you need to you know integrate
with something is going to be a web hook
trigger so you can see like you've got
your url here you have an HTT method and
this is the kind of stuff that you'd be
more familiar with setting up once you
understand the basics of um sort of
these noes but these are going to be
really powerful tools because these will
let you trigger a workflow based on um
information coming through from another
site so if you wanted if you had some
sort of form on your website and you
wanted to hook up to that and then every
time someone filled out the form you
could have this go through where it's
going to you know notify you that
someone filled out the form it's going
to send them an email sort of welcoming
them it's going to throw them into a
database and then it's going to send
slack message to your team something
like that so these web hooks can be very
powerful when it comes to automating
other processes as well and getting
pretty customized but before you get in
here and you want to start configuring
stuff I think it's just important that
you understand the the framework and the
basics of you know apis and points HTTP
requests and how all that stuff is going
to work in nadn and then you can really
explore with stuff like web hook
triggers which are very cool and offer a
lot of flexibility all right we have now
made it to part six which is going to be
the final part of this master class you
know we've covered a lot of ground we
started the basics of NN building your
first workflow we created an AI powered
agent using RG Rag and Vector databases
we made API calls and HTTP requests we
talked about extending your workflows
with custom tools and web hooks so
you're no longer a beginner you have the
tools and knowledge to create powerful
automations that are going to transform
your productivity the way you approach
your automation projects and I just
wanted to close off with talking about
error workflows um just sort of best
practices when it comes to creating
workflows in na then and sort of how you
can do that most optimally and then um
just some final next steps and just
closing thoughts
all right back in NN here we have a
error demo workflow which is just an
error workflow that I just created real
quick as you can see it's going to start
off with an error trigger so this
workflow is going to execute whenever
there's an error and another workflow
that we're hooking up this one too so
that'll make more sense once we actually
configure it but as you can see It'll
Get triggered and then it will come in
my telegram um which will be sending me
a message so it's going to notify me
that there's an error it's going to tell
me the workflow that's erroring it's
going to tell me the error message
message that happened and then it will
give me a link to the actual execution
of the error so that would all just pop
up on my telegram I can click on the
link and come in and see what's going on
but in this case this is going to be you
know a personal assistant I showed this
a little bit earlier so this is the
workflow we want to hook up to that
error workflow we come in here up top
right grab that three dots click on
settings and then right here you can see
error workflow so a second workflow to
run if the current one fails the second
workflow should have an error trigger
node so we saw the error trigger node we
saw that this workflow is called error
demo so we've hooked that up as this
error workflow and um now we just need
to make sure that this workflow is going
to error so let's just delete the brain
of the assistant so this one should
error for sure as you can see it already
is and um it's going to be calling this
and then it's going to be filling in the
information when it airs so let me just
pull up my telegram real quick as you
can see this telegram this is my AI
personal assistant so this is how we
talk to it and this is how how it talks
to us back right here if you can see
this flow but let's just ask it to do
something like can you get my
emails this should airor and as you can
see we got the error notification the
workflow is personal assistant which is
right up here the message is that a chat
model subn node must be connected so
that's the eror that's going on right
here and then we have a link to that
execution I can click on that link and
it should bring me into what exactly
just happened and we can see why it's
erroring so as you can see this is the
most recent execution that just happened
um it's going to take a second to load
up but basically the error is just
happening because a chat model sub node
must be connected that's why it aired
and as you can see that's exactly what
it told us in
here okay so that's kind of how this
works um you know you could even go you
could expand off of this so let's say
you want to get notified um but then
let's say that we want to you know send
an email to the team that says hey this
this isn't working right now um we're
working on this to get fixed so let's
just come in here and let's send a
message
um obviously we need to set up all this
information so let me do this real quick
really quickly configured this node
obviously we're sending a message put
who you want to send it to this could be
you know a ton of different emails you
got the subjects which is going to be
error and then it's going to tell us the
error message and then the message of
the email is going to say hey team we
received an error in blank workflow
we're working to resolve the issue
thanks so if I just you know test this
step we would see that that came through
um let me pull up the email real quick
so here you can see we got the error
example error message hey team we
received an error in example workflow
we're working to resolve the issue um so
let's just quickly go back and end it in
turn off the append attribution and
we'll save this and then we can um pull
up telegram we will ask it to get the
emails again and we will get an error
notification in our telegram right here
but then we should also get a new email
with that information coming through um
so we'll just give this one a sec here
okay now we got an error a chat model
sub node must be connected hey team we
receiving error and personal assistant
where we're going to resolve the issue
so obviously that's just a very quick
example email um you could configure the
subject and the message however you want
but that's just goes to show how a quick
error workflow you can set that up you
can hook it up to multiple different um
agents multiple different workflows that
you have that when they when they error
this logic will take place and you'll
get notified right away so that's just
another cool feature of net all right if
you guys have made it this far really
appreciate you sticking it through all
the way hopefully this has been a really
helpful session but before we close out
let's quickly go over some best
practices for workflow optimization to
ensure that your workflows are staying
efficient maintainable scalable and you
know all this kind of stuff as you build
out more complex automations so the
first thing I wanted to touch on real
quick is just keeping your workflows
organized as they grow you got to keep
them well organized it's going to save
you a ton of time down the road when you
realize uh oh like we have to redo this
or there's a problem and now we are all
confused about what's going on here so
make sure you use you know descriptive
node names you can throw in comments
really easy with a little sticky note um
you can you know make notes in your
workflow so that if anyone else wants to
come in and look at what's going on or
wants to help you out in the future they
can quickly understand what the workflow
is doing what each part of the workflow
is doing then we want to be able to use
sub workflows for reusability you don't
need to reinvent the wheel every single
time you want to do a similar task in
these different workflows consider
creating subw workflows like we talked
about with you know maybe one for
sending email one for creating calendar
events and then you can hook that up to
a bunch of different agents or even have
agent that's you know a specialist in
one certain type of platform so that's
going to make it sa saves you a ton of
time later down the road when you want
to make more complex automations you
don't have to build out the same you
know five nodes that are going to be the
same staple for every single thing that
you need to do in that space um so
that's just going to save you a lot of
time and avoid redundancy and the third
thing we want to do is Implement air
handling so already talked about that a
little bit but you can even take it a
step further so you know errors are
going to happen
no no workflows immune to issues like an
API failing or something like that so if
you build in some of these issues or
error handling issues it's going to
ensure that your workflows remain robust
you can be notified when something goes
wrong you'll have like a safety net
behind each of your automations and then
finally just you want to optimize for
scalability so as your workflows get
bigger efficiency is going to be super
important so you want to use features
like batch processing or pagination you
want to have a lot of conditional logic
in there in order to handle larger data
sets more complex branching workflows so
scaling doesn't just mean bigger
workflows it also means making them
smarter workflows and next steps now
that you've made it through this master
class you built a solid foundation I
encourage you to keep pushing your
boundaries there's so much more that you
can explore in nadn and your next step
should be about expanding your skills
and experimenting with sort of more
advanced templates so in order to do
this to continue growing and learning I
definitely want to invite you all to
join my fre School Community where we
all you know share ideas workflows cool
things that we've built using nadn it's
all about collaboration and inspiration
so whether you're looking for feedback
or you just want to brainstorm new ideas
or get some questions answered um it's
nice to have a very supportive Community
to share your progress with and it's
going to make the experience a lot
better so please hop in the link for
that is going to be in the description
and I'll also be sharing a lot of
resources in there that I I use in each
of the videos and stuff like that so I'd
love to talk to you guys and I'll see
you in there the first thing here is
going to be just to get in and start
building you know the power of learning
by doing is insane a
but really anything that involve tools
like n is it's best to just learn by
getting your hands dirty you know as you
build workflows experiment with things
push the boundaries of what you can
automate you're going to run into
challenges and you're going to have some
failures but that's definitely a good
thing like it's just part of the process
and when you fail and you can go in and
figure out what happened and actually
solve that problem you're going to just
understand the process way more than
someone who you know is not actually
getting in there and doing things just
watching YouTube videos stuff like that
you even every time I build out any sort
sort of agent any sort of workflow it
always fails and it's just going to
happen but that's how you really
understand you know the logic of stuff
moving through so these moments where
you're going to gain the deepest
understanding of how n in works and how
you're able to actually improve on your
skills so you know don't be afraid to
make mistakes it's just going to happen
then I would say you want to get in and
start exploring some Advanced templates
so at the beginning of this master class
we looked at sort of the community in
nadn and the template Gallery which is
just a gold mine of ideas and pre-built
workflows so you can dive into those and
you can see how people are building
things you know there's no one right way
to build an automation so you can see
different ideas and it will really help
you sort of expand your skills there too
the third thing I would say would be to
experiment with new Integrations you
know don't be afraid to try out new ones
even though naden supports over 300
Integrations for you know popular CRM
systems social media platforms databases
um it's really important to just get in
there play around with HTTP requests
different web Hooks and see all the
possibilities of how you can automate
stuff and it's really just going to you
know expand your your capabilities and
then you can always start to build and
share your own templates once you really
get experience with um building out
different things and you you start to
get more creative with your workflows so
that'll be really cool you can share
them with the community it's a great way
to inspire others and also get feedback
on the sort of builds that you're doing
and how to optimize them so yeah that is
going to be it for the master class
those of you that made it this far I
really appreciate you taking the time to
you know sit down for however long this
video was and um just listen through all
this kind of information and I really uh
try to structure it in a way where you'd
really be able to come from a beginner
and really understand what goes on in NN
and how to just get in there and start
building some simple agents and then
just make them more more complex as you
learn but like I said that's the end so
congratulations for making it this far
thank you guys so much for your time and
I will see you in that school community