Email. It's essential, obviously, but let's be
honest, for most of us, it's just chaos, pure
chaos. We talked last time about building the
AI brain, the part that sorts through it all.
Right, the classification layer. And that's huge,
don't get me wrong. But thinking, that's only
half of it. The real magic, the part that actually
gives you back hours every week, that happens
when your AI doesn't just read, but it acts.
We're basically giving the AI hands, you know,
so it can manage your inbox all the time, 24
-7, sending replies, picking the right teams,
clearing out all that junk. So that's the mission
for this deep dive. We're getting into part two
of that AI. Email Assistant Guide, focusing specifically
on building out those action branches, the hands,
as you said. Exactly, within the workflow. And
we'll be mentioning NANN quite a bit. Just quickly
for anyone unfamiliar, NANN is essentially a
no -code tool. It lets you build and automate
these kinds of workflows, linking things like
AI models to your Gmail. Yep, it's the glue.
So the plan today is pretty straightforward.
We're going to look at four key actions, kind
of tiered by risk, actually. Low -risk auto -replies
for common stuff. Then safe team notifications,
especially for sensitive things like finance.
After that, high priority draft creation where
a human still needs to press send. And finally,
the easy one. Just cleaning up the inbox. Right.
And we'll wrap up with how to actually turn it
on the activation step, which is critical. And
then some really cool ways to scale this up,
including, you know, the costs involved. Sounds
like a solid plan. Let's start with those auto
replies. The customer support branch. This feels
like where the biggest immediate time saving
could be, right? Getting rid of repetitive answers.
Oh, absolutely. Total elimination of that Q &A
grind. So once an email is classified as, say,
a support request, the next step is you add an
AI agent node. Okay, the agent node. That's where
the thinking happens. Well, the writing happens.
It needs to generate the reply content. So you
configure it to see the original email's subject
and body. It just pulls that info right from
the Gmail trigger that kicked everything off.
Makes sense. It needs the context. But how do
we stop it sounding like, well, like a robot?
How do we make sure it represents the company
properly? Ah, that's all down to the system prompt.
This is absolutely crucial. Think of it less
like a suggestion and more like the AI's hard
-coded rulebook. It's job description, basically.
You have to give it really specific rules like
define the exact tone, be friendly, professional,
empathetic, specify its expertise. You only know
about the 2024 product guide, things like that.
So you're building guardrails. What if it gets
asked something outside that guide? It can't
just make something up for you. Exactly. That's
maybe the most important guardrail. You put in
a hard rule. If you do not know the answer, stop.
Do not guess. Just tell the user, we've passed
this to a human. Please reach out to support
at company .com or whatever the real email is.
And you even specify the sign off like AI fires
AI assistant for total brand consistency. And
technically, how does that reply actually get
sent? You'd pick a good conversational model.
Something like Claude 3 .7 Sonnet is great for
this. Then in the Gmail node itself, the key
action is reply to a message. Why that specific
action? Because it keeps the reply perfectly
threaded within the original email conversation.
It looks completely natural to the person who
emailed you. Oh, and a pro tip. Disable the little
N8N attribution footer in the settings. Keeps
it looking professional. That system prompt really
does sound like the linchpin then. How critical
is getting that right for making the AI act like
a reliable company representative? Oh, it's everything.
The prompt is the AI's script. It guarantees
the brand voice and provides those essential
guardrails. All right, let's climb that ladder
of sensitivity you mentioned. Finance and billing
emails definitely don't want AI auto -replying
there. What's the safe play? Yeah, absolutely
not. The goal here is different. It's safe. professional
human notification, instantly letting the right
team, maybe finance at your company .com, know
something needs their attention. But crucially,
we do not want the AI generating any content
here. So this branch actually skips the AI agent
node entirely. Interesting. So you're deliberately
not using the AI here, even though it's an AI
workflow. Is it just to save a few pennies on
API calls? That's a tiny part of it, maybe. But
the main reason, it's risk, compliance risk,
accuracy risk. LLMs can hallucinate, right? You
just cannot risk the AI misreading an account
number or a payment amount or a due date. Finance
stuff needs humanized, period. Accuracy and safety
first. Right. So the potential downside of an
AI mistake, a hallucination, is just way too
high when money's involved. Makes sense. Totally.
So technically it's simple. You just use a standard
Gmail send node. You're basically just forwarding
the key info. You build a clean little report
in the body of that email, the one going to your
internal team. Use dynamic expressions to pull
in, say, the sender's name, their email address,
the original subject. Makes it super easy for
the finance team to see what's up at a glance.
So beyond saving a bit on API costs. The expert
level thinking for avoiding AI generation in
finance is really about safety and compliance.
Absolutely. Money requires human review. Notifications
prioritize accuracy and safety over instant generated
replies. Okay, now for that middle ground. High
priority emails. Things that need a fast response,
but definitely need a human to sign off before
it goes out. How do we blend AI speed with human
control here? Yeah, this is a really common need.
The strategy is straightforward. Let the AI draft
the response, but a human reviews potentially
tweaks it and then hit send. So another AI agent
node. Yep. Another AI agent likely configured
differently. Maybe you want a more formal or
detailed tone for these high priority messages.
The system prompt here is, again, absolutely
key to getting that right. You know, it's funny,
even doing this stuff regularly, I still wrestle
with prompt drift sometimes trying to get that
tone just right. It's amazing how one small change
of the prompt can totally change. the AI's personality.
Oh, tell me about it. It's incredibly sensitive.
That prompt basically sits the AI's entire character.
We've seen people build wild system prompts just
for testing. I remember one called Mr. Apocalypse.
The goal was to make the AI brutally honest,
even rude. Slash slightly. Wow. Shows how much
control you have. But for a serious high priority
response, you'd use a powerful model, maybe Claude
Sonnet again, and a prompt that demands careful
synthesis, maybe referencing specific internal
policies if you have RG set up, which we can
talk about later. OK, so the AI writes this perfect
context aware response. How does it get queued
up for the human review? it physically appear
right the magic technical bit in the final gmail
node for this branch you select the action create
a draft simple enough but here's the detail that
tricks people up constantly you must map the
threaded from the original email trigger into
the draft id field of that action hold on threaded
not message id for folks may be less familiar
with email apis why is that specific id so important
what happens if you accidentally use message
id Good question. If you use MessageEyed, yeah,
it'll probably create a draft. But often it's
like disconnected. It just shows up as a brand
new standalone email in your drafts folder, completely
separate from the original conversation. But
when you use the threaded... You're linking that
AI -generated draft directly to the entire conversation.
So when you open the original email, boom, the
draft is sitting right there ready to go in context.
You can just review it, maybe tweak a word, and
hit send. It keeps the flow perfect. So threaded
is the key. Why is using the threaded so critical
versus the message -eyed? Threaded links the
draft to the whole conversation, making it ready
for a quick context -aware send. Okay, let's
hit the easiest one now, the promotional emails.
Yeah. The junk mail branch. Yeah. This sounds
less about complex actions and more about satisfaction.
Chuckles. Yeah, this is the simplest but maybe
the most satisfying part. Pure inbox cleanup,
reducing that cognitive load. Once an email gets
classified as promotion, the action is super
quick. Two steps. First, you maybe add a promotion
label just for organization. Then immediately
after, you use another Gmail node with the action
mark a message as read. That's it. That's it.
Just mark as read. It sounds almost too simple.
Are there any hidden downsides? Does marking
it read immediately cause any problems? Honestly,
very few downsides for true promotional stuff.
The email still gets filed away by the label
if you used one, so you can find it later if
you really need to. The main goal here is psychological.
How so? It stops that email from contributing
to the unread count. You know, that little red
badge that causes so much anxiety. Yeah. This
just removes that low value interruption from
your immediate attention. You basically set it
and forget it. It tidies things up automatically.
So this sounds almost too simple. Are there any
drawbacks to immediately marking promotional
mail as read? Minimal risk, high satisfaction.
The goal is removing low value interruptions
from the main view. Right then. We've built out
all four paths. The auto replies, the team notifications,
the drafts, and the cleanup crew for promotions.
We have this whole sophisticated workflow sitting
there, but it's not actually doing anything yet,
is it? Exactly right. Everything we've described
so far only runs if you go into NAN and manually
click execute workflow, which isn't very automated.
No. The final absolutely essential step is you
go to the top right corner of the NANN workflow
editor and you find the toggle switch that says
inactive. You have to flip that switch to active.
Yeah, the big green switch. That's the one. That
tells the system, OK, now you're live. Start
checking Gmail automatically. Usually it checks
every minute by default for new emails and run
this process. OK, so it's active. It's checking
every minute. How do we know it's working correctly?
Like, how do we check if it's actually sending
emails down the right paths? Good call. You need
to monitor it, especially at first. Inside NEN,
there's a tab called Executions. That's your
command center, your audit log. Every time the
workflow runs because a new email came in, it
logs an execution. You can click into each one
and visually see the path it took. Did it go
down the support branch? Did it correctly identify
a finance email and just send the notification?
It's how you verify and troubleshoot. When people
first build something like this, what's the most
common, maybe... slightly embarrassing mistake
they make right after building it that stops
it from working? Honestly, forgetting to flip
that switch. They build this amazing thing, spend
hours on it, and then wonder why nothing's happening.
It's just sitting there, inactive. So what is
the most common, simple mistake people make right
after building the workflow? Forgetting to switch
the workflow toggle from inactive to active,
it's a manual setup detail. Okay, so once you
have that basic system humming along nicely,
filtering emails, sending replies, creating drafts,
then you can start thinking bigger. How do we
upgrade this from just an assistant to more like
a chief of staff for your inbox? Scaling up.
I like the sound of that. Where do we start?
What are the big enhancements? Okay, three key
things come to mind. First, build a proper logging
system. Think of it like the black box recorder
for your workflow. How does that work? At the
end of every single one of those action branches
we just discussed, the reply, the notification,
the draft, the cleanup, you add one final node,
maybe a Google Sheets node or a database node
if you prefer. And in that node, you log key
details, the timestamp. which category the email
fell into, maybe the sender. And if an AI response
was generated, log the actual response text.
This creates a permanent record and audit trail.
You can see exactly what the AI is doing over
time. Invaluable for improvement. That sounds
powerful for tracking. What's next? Yeah. How
do we make the AI itself smarter? Right. Connect
knowledge bases. This is where you implement
RA. That's retrieval augmented generation. RA.
We hear that term a lot. Break it down simply.
What does RA let the AI do? Basically, RIG lets
your AI look things up in your own documents
before it answers. Instead of just relying on
its general knowledge from its training data,
it can consult your specific internal help docs,
product manuals, policy guides, whatever you
give it. Ah. So it stops guessing about company
-specific stuff. Precisely. It uses a vector
store that's a special database, like Supabase
or Pinecone, to hold your documents in a way
the AI can quickly search. It finds the relevant
passages from your info and uses that to construct
the answer. Massive quality improvement. Okay.
Logging for monitoring, RAG for accuracy. What's
the third? A -B testing models. Don't just pick
one AI model and stick with it forever. Models
evolve, prices change. Set up a simple splinter
node early in the workflow. Send, say, 50 % of
incoming emails to a cheaper, faster model, maybe
like GPT 4 .1 Mini. Send the other 50 % to a
more powerful, maybe slightly more expensive
model, like Claude 3 .7 Sonnet. Then... use that
logging system we just talked about log which
model handled which email and maybe even track
the quality or if a draft needed editing over
time you get real data on which model gives you
the best bang for your buck for different types
of emails whoa hang on if you combine that logging
with the a b testing You could scale that across
a whole company. Use the logs to scientifically
figure out the absolute best, most cost -effective
model for every single different email task.
That's genuinely data -driven efficiency right
there. Exactly. Optimizing cost and quality based
on real results. How quickly does connecting
a knowledge base with our ag actually change
the quality? Is it noticeable right away? Immediately.
The agent shifts from potentially guessing to
actually looking up facts from your own internal
docs. It's night and day for accuracy on specific
topics. OK, but it's tech. Things will go wrong
sometimes. Yeah. What are the usual problems
people run into and how do they fix them quickly?
Yeah, inevitably there's some turbulence. We
usually see kind of a big four. First is misclassification
emails going down the wrong path. The fix there
is usually refining those category descriptions
in your initial classifier. Add more specific
keywords, maybe better examples. Okay, what else?
Generic responses. If the AI's replies sound
bland or unhelpful, that points straight back
to the system prompt for that AI agent. You need
to inject more detail, more personality, more
specific company context. Make the prompt richer.
We talked about drafts earlier. What if the drafts
aren't linking properly to the original email
thread? 99 % of the time, that's the threaded
issue again. Go back to that create draft action
in the Gmail node. Double, triple check that
you've correctly mapped the threaded from the
trigger into the draft ID field, not the message
ID. That's almost always the culprit. Got it.
Threaded. And the fourth common issue. Credentials
errors or the workflow just stopping. This could
be simple, like needing to reauthenticate your
Google connection in N8NN. Or it could be you've
run out of API credits with your AI provider,
like OpenRouter or OpenAI. Always check your
billing dashboard if things just stop working.
So if a user's workflow just dies unexpectedly,
what's the very first thing they should check
before diving into complex debugging? Honestly,
confirm that active toggle switch is still green.
It sounds silly, but sometimes things can get
accidentally deactivated. It's the simplest possible
fix for what looks like a dead workflow. This
all sounds incredibly powerful. A 2047 AI assistant
managing email must cost a fortune, right? Let's
talk ROI. What's the actual cost here? You'd
be surprised. It's actually very accessible.
Let's break it down. You've got your base platform
cost, so the NAN cloud plan you'd likely need
is around, say, $25 a month. That's fixed. Okay.
Then you have the variable cost. the AI API calls.
This is pay as you go. But the key is, remember
how we use cheaper, faster models like GPT 4
.1 mini for that initial high volume classification
step? Right. Most emails just get sorted. Exactly.
That keeps the cost way down. So even if you're
processing, let's say, 100 emails every single
day. Yeah. Give me a real world number for that
kind of volume. With this kind of optimized setup,
you're probably looking at only about... 50 cents
to maybe a dollar per day in total API fees.
Seriously, that low? Yeah. So add that up for
a month, maybe $15 to $30 max for the AI usage.
Your total all -in cost per month for this whole
system, realistically, somewhere around $40 to
$55. Okay, hold on. $55 a month sounds amazing,
almost too good to be true, but that's just the
software and API cost. What about the human time?
Isn't there a hidden cost in setting this up,
tweaking the prompts? fixing things when they
break that's a really fair point yes there's
an initial time investment you got to build it
right maybe budget i don't know three to five
hours for the initial setup and the first week
of watching it tuning it okay but once it's stable
it really does mostly run itself maybe occasional
tweaks so you weigh that initial time cost and
the 55 a month against the time it saves you
if this thing genuinely handles tasks that used
to take you five maybe 10, even more hours every
single week. Yeah. That $55 looks pretty insignificant
pretty quickly. The ROI is usually exceptional,
often within the first month or two. And that
minimal running cost, is that mostly because
we're being smart about using cheaper AI for
the bulk classification work? Yes, absolutely.
Optimizing model choice by task is the key. It
keeps those API costs extremely low while still
using powerful models where you really need quality.
Wow. OK, so we've really seen this technical
guy transform, haven't we? It's gone from just
sorting emails into a genuinely operational system,
one that sends replies, preps drafts for you,
alerts the right people and just cleans up the
clutter. Yeah, it's about building structure.
This AI assistant, when set up right, it delivers
organization, sure, but also like genuine peace
of mind. And yeah, critically, it really does
give you back those five or 10 hours every week,
the gap between email chaos and having this like.
personal 247 chief of staff. It's just a bit
of setup time and that's surprisingly small monthly
cost. So for everyone listening, maybe take a
minute after this to think about your own email
habits. Where are those repetitive tasks? Where
does all the time go? Start thinking about which
action branches you'd build first. And that brings
us to our final thought. A bit provocative, maybe.
If you actually manage to delegate 10 hours of
email work every single week using a system like
this, what's the one big, important, high -value
project you'd finally have the bandwidth, the
focus, the time to really dedicate yourself to?
What becomes possible then? At UTR, we'll music.