WEBVTT

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Email. It's essential, obviously, but let's be

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honest, for most of us, it's just chaos, pure

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chaos. We talked last time about building the

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AI brain, the part that sorts through it all.

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Right, the classification layer. And that's huge,

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don't get me wrong. But thinking, that's only

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half of it. The real magic, the part that actually

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gives you back hours every week, that happens

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when your AI doesn't just read, but it acts.

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We're basically giving the AI hands, you know,

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so it can manage your inbox all the time, 24

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-7, sending replies, picking the right teams,

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clearing out all that junk. So that's the mission

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for this deep dive. We're getting into part two

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of that AI. Email Assistant Guide, focusing specifically

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on building out those action branches, the hands,

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as you said. Exactly, within the workflow. And

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we'll be mentioning NANN quite a bit. Just quickly

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for anyone unfamiliar, NANN is essentially a

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no -code tool. It lets you build and automate

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these kinds of workflows, linking things like

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AI models to your Gmail. Yep, it's the glue.

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So the plan today is pretty straightforward.

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We're going to look at four key actions, kind

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of tiered by risk, actually. Low -risk auto -replies

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for common stuff. Then safe team notifications,

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especially for sensitive things like finance.

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After that, high priority draft creation where

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a human still needs to press send. And finally,

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the easy one. Just cleaning up the inbox. Right.

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And we'll wrap up with how to actually turn it

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on the activation step, which is critical. And

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then some really cool ways to scale this up,

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including, you know, the costs involved. Sounds

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like a solid plan. Let's start with those auto

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replies. The customer support branch. This feels

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like where the biggest immediate time saving

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could be, right? Getting rid of repetitive answers.

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Oh, absolutely. Total elimination of that Q &A

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grind. So once an email is classified as, say,

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a support request, the next step is you add an

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AI agent node. Okay, the agent node. That's where

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the thinking happens. Well, the writing happens.

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It needs to generate the reply content. So you

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configure it to see the original email's subject

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and body. It just pulls that info right from

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the Gmail trigger that kicked everything off.

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Makes sense. It needs the context. But how do

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we stop it sounding like, well, like a robot?

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How do we make sure it represents the company

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properly? Ah, that's all down to the system prompt.

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This is absolutely crucial. Think of it less

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like a suggestion and more like the AI's hard

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-coded rulebook. It's job description, basically.

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You have to give it really specific rules like

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define the exact tone, be friendly, professional,

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empathetic, specify its expertise. You only know

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about the 2024 product guide, things like that.

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So you're building guardrails. What if it gets

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asked something outside that guide? It can't

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just make something up for you. Exactly. That's

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maybe the most important guardrail. You put in

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a hard rule. If you do not know the answer, stop.

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Do not guess. Just tell the user, we've passed

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this to a human. Please reach out to support

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at company .com or whatever the real email is.

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And you even specify the sign off like AI fires

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AI assistant for total brand consistency. And

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technically, how does that reply actually get

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sent? You'd pick a good conversational model.

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Something like Claude 3 .7 Sonnet is great for

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this. Then in the Gmail node itself, the key

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action is reply to a message. Why that specific

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action? Because it keeps the reply perfectly

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threaded within the original email conversation.

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It looks completely natural to the person who

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emailed you. Oh, and a pro tip. Disable the little

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N8N attribution footer in the settings. Keeps

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it looking professional. That system prompt really

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does sound like the linchpin then. How critical

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is getting that right for making the AI act like

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a reliable company representative? Oh, it's everything.

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The prompt is the AI's script. It guarantees

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the brand voice and provides those essential

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guardrails. All right, let's climb that ladder

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of sensitivity you mentioned. Finance and billing

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emails definitely don't want AI auto -replying

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there. What's the safe play? Yeah, absolutely

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not. The goal here is different. It's safe. professional

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human notification, instantly letting the right

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team, maybe finance at your company .com, know

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something needs their attention. But crucially,

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we do not want the AI generating any content

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here. So this branch actually skips the AI agent

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node entirely. Interesting. So you're deliberately

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not using the AI here, even though it's an AI

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workflow. Is it just to save a few pennies on

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API calls? That's a tiny part of it, maybe. But

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the main reason, it's risk, compliance risk,

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accuracy risk. LLMs can hallucinate, right? You

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just cannot risk the AI misreading an account

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number or a payment amount or a due date. Finance

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stuff needs humanized, period. Accuracy and safety

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first. Right. So the potential downside of an

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AI mistake, a hallucination, is just way too

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high when money's involved. Makes sense. Totally.

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So technically it's simple. You just use a standard

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Gmail send node. You're basically just forwarding

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the key info. You build a clean little report

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in the body of that email, the one going to your

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internal team. Use dynamic expressions to pull

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in, say, the sender's name, their email address,

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the original subject. Makes it super easy for

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the finance team to see what's up at a glance.

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So beyond saving a bit on API costs. The expert

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level thinking for avoiding AI generation in

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finance is really about safety and compliance.

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Absolutely. Money requires human review. Notifications

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prioritize accuracy and safety over instant generated

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replies. Okay, now for that middle ground. High

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priority emails. Things that need a fast response,

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but definitely need a human to sign off before

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it goes out. How do we blend AI speed with human

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control here? Yeah, this is a really common need.

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The strategy is straightforward. Let the AI draft

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the response, but a human reviews potentially

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tweaks it and then hit send. So another AI agent

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node. Yep. Another AI agent likely configured

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differently. Maybe you want a more formal or

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detailed tone for these high priority messages.

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The system prompt here is, again, absolutely

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key to getting that right. You know, it's funny,

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even doing this stuff regularly, I still wrestle

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with prompt drift sometimes trying to get that

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tone just right. It's amazing how one small change

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of the prompt can totally change. the AI's personality.

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Oh, tell me about it. It's incredibly sensitive.

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That prompt basically sits the AI's entire character.

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We've seen people build wild system prompts just

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for testing. I remember one called Mr. Apocalypse.

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The goal was to make the AI brutally honest,

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even rude. Slash slightly. Wow. Shows how much

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control you have. But for a serious high priority

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response, you'd use a powerful model, maybe Claude

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Sonnet again, and a prompt that demands careful

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synthesis, maybe referencing specific internal

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policies if you have RG set up, which we can

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talk about later. OK, so the AI writes this perfect

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context aware response. How does it get queued

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up for the human review? it physically appear

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right the magic technical bit in the final gmail

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node for this branch you select the action create

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a draft simple enough but here's the detail that

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tricks people up constantly you must map the

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threaded from the original email trigger into

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the draft id field of that action hold on threaded

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not message id for folks may be less familiar

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with email apis why is that specific id so important

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what happens if you accidentally use message

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id Good question. If you use MessageEyed, yeah,

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it'll probably create a draft. But often it's

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like disconnected. It just shows up as a brand

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new standalone email in your drafts folder, completely

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separate from the original conversation. But

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when you use the threaded... You're linking that

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AI -generated draft directly to the entire conversation.

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So when you open the original email, boom, the

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draft is sitting right there ready to go in context.

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You can just review it, maybe tweak a word, and

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hit send. It keeps the flow perfect. So threaded

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is the key. Why is using the threaded so critical

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versus the message -eyed? Threaded links the

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draft to the whole conversation, making it ready

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for a quick context -aware send. Okay, let's

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hit the easiest one now, the promotional emails.

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Yeah. The junk mail branch. Yeah. This sounds

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less about complex actions and more about satisfaction.

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Chuckles. Yeah, this is the simplest but maybe

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the most satisfying part. Pure inbox cleanup,

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reducing that cognitive load. Once an email gets

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classified as promotion, the action is super

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quick. Two steps. First, you maybe add a promotion

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label just for organization. Then immediately

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after, you use another Gmail node with the action

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mark a message as read. That's it. That's it.

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Just mark as read. It sounds almost too simple.

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Are there any hidden downsides? Does marking

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it read immediately cause any problems? Honestly,

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very few downsides for true promotional stuff.

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The email still gets filed away by the label

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if you used one, so you can find it later if

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you really need to. The main goal here is psychological.

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How so? It stops that email from contributing

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to the unread count. You know, that little red

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badge that causes so much anxiety. Yeah. This

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just removes that low value interruption from

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your immediate attention. You basically set it

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and forget it. It tidies things up automatically.

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So this sounds almost too simple. Are there any

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drawbacks to immediately marking promotional

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mail as read? Minimal risk, high satisfaction.

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The goal is removing low value interruptions

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from the main view. Right then. We've built out

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all four paths. The auto replies, the team notifications,

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the drafts, and the cleanup crew for promotions.

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We have this whole sophisticated workflow sitting

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there, but it's not actually doing anything yet,

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is it? Exactly right. Everything we've described

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so far only runs if you go into NAN and manually

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click execute workflow, which isn't very automated.

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No. The final absolutely essential step is you

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go to the top right corner of the NANN workflow

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editor and you find the toggle switch that says

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inactive. You have to flip that switch to active.

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Yeah, the big green switch. That's the one. That

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tells the system, OK, now you're live. Start

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checking Gmail automatically. Usually it checks

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every minute by default for new emails and run

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this process. OK, so it's active. It's checking

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every minute. How do we know it's working correctly?

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Like, how do we check if it's actually sending

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emails down the right paths? Good call. You need

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to monitor it, especially at first. Inside NEN,

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there's a tab called Executions. That's your

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command center, your audit log. Every time the

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workflow runs because a new email came in, it

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logs an execution. You can click into each one

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and visually see the path it took. Did it go

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down the support branch? Did it correctly identify

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a finance email and just send the notification?

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It's how you verify and troubleshoot. When people

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first build something like this, what's the most

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common, maybe... slightly embarrassing mistake

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they make right after building it that stops

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it from working? Honestly, forgetting to flip

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that switch. They build this amazing thing, spend

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hours on it, and then wonder why nothing's happening.

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It's just sitting there, inactive. So what is

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the most common, simple mistake people make right

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after building the workflow? Forgetting to switch

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the workflow toggle from inactive to active,

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it's a manual setup detail. Okay, so once you

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have that basic system humming along nicely,

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filtering emails, sending replies, creating drafts,

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then you can start thinking bigger. How do we

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upgrade this from just an assistant to more like

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a chief of staff for your inbox? Scaling up.

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I like the sound of that. Where do we start?

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What are the big enhancements? Okay, three key

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things come to mind. First, build a proper logging

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system. Think of it like the black box recorder

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for your workflow. How does that work? At the

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end of every single one of those action branches

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we just discussed, the reply, the notification,

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the draft, the cleanup, you add one final node,

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maybe a Google Sheets node or a database node

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if you prefer. And in that node, you log key

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details, the timestamp. which category the email

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fell into, maybe the sender. And if an AI response

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was generated, log the actual response text.

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This creates a permanent record and audit trail.

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You can see exactly what the AI is doing over

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time. Invaluable for improvement. That sounds

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powerful for tracking. What's next? Yeah. How

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do we make the AI itself smarter? Right. Connect

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knowledge bases. This is where you implement

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RA. That's retrieval augmented generation. RA.

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We hear that term a lot. Break it down simply.

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What does RA let the AI do? Basically, RIG lets

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your AI look things up in your own documents

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before it answers. Instead of just relying on

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its general knowledge from its training data,

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it can consult your specific internal help docs,

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product manuals, policy guides, whatever you

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give it. Ah. So it stops guessing about company

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-specific stuff. Precisely. It uses a vector

00:12:22.610 --> 00:12:24.690
store that's a special database, like Supabase

00:12:24.690 --> 00:12:26.970
or Pinecone, to hold your documents in a way

00:12:26.970 --> 00:12:29.570
the AI can quickly search. It finds the relevant

00:12:29.570 --> 00:12:31.970
passages from your info and uses that to construct

00:12:31.970 --> 00:12:35.049
the answer. Massive quality improvement. Okay.

00:12:35.149 --> 00:12:38.370
Logging for monitoring, RAG for accuracy. What's

00:12:38.370 --> 00:12:41.590
the third? A -B testing models. Don't just pick

00:12:41.590 --> 00:12:44.289
one AI model and stick with it forever. Models

00:12:44.289 --> 00:12:47.169
evolve, prices change. Set up a simple splinter

00:12:47.169 --> 00:12:50.970
node early in the workflow. Send, say, 50 % of

00:12:50.970 --> 00:12:53.309
incoming emails to a cheaper, faster model, maybe

00:12:53.309 --> 00:12:57.049
like GPT 4 .1 Mini. Send the other 50 % to a

00:12:57.049 --> 00:12:58.830
more powerful, maybe slightly more expensive

00:12:58.830 --> 00:13:02.909
model, like Claude 3 .7 Sonnet. Then... use that

00:13:02.909 --> 00:13:05.049
logging system we just talked about log which

00:13:05.049 --> 00:13:07.889
model handled which email and maybe even track

00:13:07.889 --> 00:13:11.250
the quality or if a draft needed editing over

00:13:11.250 --> 00:13:14.049
time you get real data on which model gives you

00:13:14.049 --> 00:13:16.029
the best bang for your buck for different types

00:13:16.029 --> 00:13:19.190
of emails whoa hang on if you combine that logging

00:13:19.190 --> 00:13:22.009
with the a b testing You could scale that across

00:13:22.009 --> 00:13:24.409
a whole company. Use the logs to scientifically

00:13:24.409 --> 00:13:27.409
figure out the absolute best, most cost -effective

00:13:27.409 --> 00:13:29.769
model for every single different email task.

00:13:30.049 --> 00:13:33.090
That's genuinely data -driven efficiency right

00:13:33.090 --> 00:13:36.210
there. Exactly. Optimizing cost and quality based

00:13:36.210 --> 00:13:38.389
on real results. How quickly does connecting

00:13:38.389 --> 00:13:41.210
a knowledge base with our ag actually change

00:13:41.210 --> 00:13:44.159
the quality? Is it noticeable right away? Immediately.

00:13:44.279 --> 00:13:46.960
The agent shifts from potentially guessing to

00:13:46.960 --> 00:13:49.039
actually looking up facts from your own internal

00:13:49.039 --> 00:13:51.600
docs. It's night and day for accuracy on specific

00:13:51.600 --> 00:13:54.139
topics. OK, but it's tech. Things will go wrong

00:13:54.139 --> 00:13:56.200
sometimes. Yeah. What are the usual problems

00:13:56.200 --> 00:13:58.259
people run into and how do they fix them quickly?

00:13:58.649 --> 00:14:00.730
Yeah, inevitably there's some turbulence. We

00:14:00.730 --> 00:14:03.710
usually see kind of a big four. First is misclassification

00:14:03.710 --> 00:14:06.230
emails going down the wrong path. The fix there

00:14:06.230 --> 00:14:08.509
is usually refining those category descriptions

00:14:08.509 --> 00:14:11.110
in your initial classifier. Add more specific

00:14:11.110 --> 00:14:13.490
keywords, maybe better examples. Okay, what else?

00:14:13.750 --> 00:14:17.210
Generic responses. If the AI's replies sound

00:14:17.210 --> 00:14:20.350
bland or unhelpful, that points straight back

00:14:20.350 --> 00:14:23.250
to the system prompt for that AI agent. You need

00:14:23.250 --> 00:14:25.830
to inject more detail, more personality, more

00:14:25.830 --> 00:14:29.470
specific company context. Make the prompt richer.

00:14:29.769 --> 00:14:32.230
We talked about drafts earlier. What if the drafts

00:14:32.230 --> 00:14:34.690
aren't linking properly to the original email

00:14:34.690 --> 00:14:38.230
thread? 99 % of the time, that's the threaded

00:14:38.230 --> 00:14:41.009
issue again. Go back to that create draft action

00:14:41.009 --> 00:14:43.620
in the Gmail node. Double, triple check that

00:14:43.620 --> 00:14:45.860
you've correctly mapped the threaded from the

00:14:45.860 --> 00:14:48.320
trigger into the draft ID field, not the message

00:14:48.320 --> 00:14:50.240
ID. That's almost always the culprit. Got it.

00:14:50.360 --> 00:14:53.879
Threaded. And the fourth common issue. Credentials

00:14:53.879 --> 00:14:56.360
errors or the workflow just stopping. This could

00:14:56.360 --> 00:14:58.580
be simple, like needing to reauthenticate your

00:14:58.580 --> 00:15:01.919
Google connection in N8NN. Or it could be you've

00:15:01.919 --> 00:15:04.340
run out of API credits with your AI provider,

00:15:04.539 --> 00:15:07.379
like OpenRouter or OpenAI. Always check your

00:15:07.379 --> 00:15:09.340
billing dashboard if things just stop working.

00:15:09.799 --> 00:15:12.360
So if a user's workflow just dies unexpectedly,

00:15:12.879 --> 00:15:14.679
what's the very first thing they should check

00:15:14.679 --> 00:15:17.419
before diving into complex debugging? Honestly,

00:15:17.700 --> 00:15:20.559
confirm that active toggle switch is still green.

00:15:20.740 --> 00:15:23.139
It sounds silly, but sometimes things can get

00:15:23.139 --> 00:15:26.059
accidentally deactivated. It's the simplest possible

00:15:26.059 --> 00:15:28.980
fix for what looks like a dead workflow. This

00:15:28.980 --> 00:15:33.340
all sounds incredibly powerful. A 2047 AI assistant

00:15:33.340 --> 00:15:37.509
managing email must cost a fortune, right? Let's

00:15:37.509 --> 00:15:39.809
talk ROI. What's the actual cost here? You'd

00:15:39.809 --> 00:15:41.509
be surprised. It's actually very accessible.

00:15:41.730 --> 00:15:43.830
Let's break it down. You've got your base platform

00:15:43.830 --> 00:15:46.529
cost, so the NAN cloud plan you'd likely need

00:15:46.529 --> 00:15:49.110
is around, say, $25 a month. That's fixed. Okay.

00:15:49.210 --> 00:15:52.759
Then you have the variable cost. the AI API calls.

00:15:53.019 --> 00:15:55.899
This is pay as you go. But the key is, remember

00:15:55.899 --> 00:15:58.460
how we use cheaper, faster models like GPT 4

00:15:58.460 --> 00:16:00.840
.1 mini for that initial high volume classification

00:16:00.840 --> 00:16:03.500
step? Right. Most emails just get sorted. Exactly.

00:16:03.779 --> 00:16:06.440
That keeps the cost way down. So even if you're

00:16:06.440 --> 00:16:08.419
processing, let's say, 100 emails every single

00:16:08.419 --> 00:16:10.440
day. Yeah. Give me a real world number for that

00:16:10.440 --> 00:16:12.960
kind of volume. With this kind of optimized setup,

00:16:13.080 --> 00:16:15.500
you're probably looking at only about... 50 cents

00:16:15.500 --> 00:16:18.039
to maybe a dollar per day in total API fees.

00:16:18.279 --> 00:16:21.000
Seriously, that low? Yeah. So add that up for

00:16:21.000 --> 00:16:24.299
a month, maybe $15 to $30 max for the AI usage.

00:16:24.539 --> 00:16:27.340
Your total all -in cost per month for this whole

00:16:27.340 --> 00:16:30.100
system, realistically, somewhere around $40 to

00:16:30.100 --> 00:16:33.759
$55. Okay, hold on. $55 a month sounds amazing,

00:16:33.820 --> 00:16:36.200
almost too good to be true, but that's just the

00:16:36.200 --> 00:16:38.820
software and API cost. What about the human time?

00:16:39.259 --> 00:16:42.759
Isn't there a hidden cost in setting this up,

00:16:42.799 --> 00:16:45.289
tweaking the prompts? fixing things when they

00:16:45.289 --> 00:16:47.549
break that's a really fair point yes there's

00:16:47.549 --> 00:16:49.049
an initial time investment you got to build it

00:16:49.049 --> 00:16:51.049
right maybe budget i don't know three to five

00:16:51.049 --> 00:16:52.990
hours for the initial setup and the first week

00:16:52.990 --> 00:16:55.330
of watching it tuning it okay but once it's stable

00:16:55.330 --> 00:16:57.389
it really does mostly run itself maybe occasional

00:16:57.389 --> 00:17:00.169
tweaks so you weigh that initial time cost and

00:17:00.169 --> 00:17:02.450
the 55 a month against the time it saves you

00:17:02.450 --> 00:17:05.490
if this thing genuinely handles tasks that used

00:17:05.490 --> 00:17:09.250
to take you five maybe 10, even more hours every

00:17:09.250 --> 00:17:12.089
single week. Yeah. That $55 looks pretty insignificant

00:17:12.089 --> 00:17:14.910
pretty quickly. The ROI is usually exceptional,

00:17:15.150 --> 00:17:17.170
often within the first month or two. And that

00:17:17.170 --> 00:17:19.450
minimal running cost, is that mostly because

00:17:19.450 --> 00:17:22.549
we're being smart about using cheaper AI for

00:17:22.549 --> 00:17:25.150
the bulk classification work? Yes, absolutely.

00:17:25.609 --> 00:17:28.349
Optimizing model choice by task is the key. It

00:17:28.349 --> 00:17:31.569
keeps those API costs extremely low while still

00:17:31.569 --> 00:17:33.809
using powerful models where you really need quality.

00:17:34.150 --> 00:17:37.319
Wow. OK, so we've really seen this technical

00:17:37.319 --> 00:17:39.559
guy transform, haven't we? It's gone from just

00:17:39.559 --> 00:17:42.740
sorting emails into a genuinely operational system,

00:17:42.859 --> 00:17:46.160
one that sends replies, preps drafts for you,

00:17:46.279 --> 00:17:48.619
alerts the right people and just cleans up the

00:17:48.619 --> 00:17:50.279
clutter. Yeah, it's about building structure.

00:17:50.420 --> 00:17:53.140
This AI assistant, when set up right, it delivers

00:17:53.140 --> 00:17:56.059
organization, sure, but also like genuine peace

00:17:56.059 --> 00:17:58.420
of mind. And yeah, critically, it really does

00:17:58.420 --> 00:18:00.500
give you back those five or 10 hours every week,

00:18:00.519 --> 00:18:03.220
the gap between email chaos and having this like.

00:18:03.549 --> 00:18:06.009
personal 247 chief of staff. It's just a bit

00:18:06.009 --> 00:18:08.009
of setup time and that's surprisingly small monthly

00:18:08.009 --> 00:18:10.230
cost. So for everyone listening, maybe take a

00:18:10.230 --> 00:18:12.029
minute after this to think about your own email

00:18:12.029 --> 00:18:14.309
habits. Where are those repetitive tasks? Where

00:18:14.309 --> 00:18:15.869
does all the time go? Start thinking about which

00:18:15.869 --> 00:18:18.309
action branches you'd build first. And that brings

00:18:18.309 --> 00:18:20.789
us to our final thought. A bit provocative, maybe.

00:18:21.109 --> 00:18:23.670
If you actually manage to delegate 10 hours of

00:18:23.670 --> 00:18:26.109
email work every single week using a system like

00:18:26.109 --> 00:18:29.029
this, what's the one big, important, high -value

00:18:29.029 --> 00:18:31.470
project you'd finally have the bandwidth, the

00:18:31.470 --> 00:18:33.849
focus, the time to really dedicate yourself to?

00:18:33.950 --> 00:18:36.750
What becomes possible then? At UTR, we'll music.
