What Are ChatGPT Connectors?

Topics Covered in This Episode:

1. ChatGPT Connectors Overview & Basics
2. Connectors vs. Traditional RAG Comparison
3. Setting Up and Using ChatGPT Connectors
4. Supported ChatGPT Connector Integrations List
5. ChatGPT Connectors Security and Permissions
6. Modes: Chat, Deep Research, Agent, Synced
7. Real-Time Data Access with Connectors
8. Multi-Connector Workflow and Productivity Tips
9. Business Use Cases for Connectors
10. Connectors vs. RAG: Cost and Deployment
11. Model Context Protocol (MCP) & Custom Connectors
12. Time-Saving Connector Examples and Demonstrations
13. Connector Best Practices for Enterprises

Keywords:

ChatGPT connectors, ChatGPT connector, connectors for ChatGPT, AI connectors, custom connectors, model context protocol, MCP, secure bridges, business data integration, ChatGPT integration, data sources, Google Drive connector, Outlook connector, SharePoint connector, HubSpot connector, deep research, chat search, synced connectors, agent mode, AI agents, retrieval augmented generation, RAG, mini RAG, context engineering, context sharing, proprietary data, enterprise AI, AI for business, ChatGPT for business, productivity tools, AI assistant, data security, SSO authentication, data permissions, AI context management, file lookup, email integration, CRM integration, Google Calendar connector, Dropbox connector, Notion connector, GitHub connector, Teams connector, OneDrive integration, live data fetch, actionable insights, business automation, reducing hallucinations, AI grounded in company data, multi-source analysis, AI-powered workflows, AI for HR, compliance automation, data silos, cited responses, AI reporting, productivity gains.

Podcast Transcript

Jordan Wilson [00:00:14]:
Remember when RAG was all the rage in AI? I'm talking about retrieval augmented generation. Essentially, when many companies would spend multiple 6 or 7 figures and many times half a year, a year or more of development time just to connect their company's proprietary data to large language models. Yeah. It's a little different than what a chat GPT connector is, but it essentially does that for as little as $20 a month in about twenty seconds. You can get a good portion of what many enterprise companies spent, like I said, countless dollars and countless hours on. Yet, I'm surprised. I do a lot of consulting for a lot of enterprise companies. You know, those that are, investing heavily in chat GPT enterprise licenses, and they need to train thousands of employees.

Jordan Wilson [00:01:08]:
And yet so many of them still aren't even using these chat GPT connectors. So that's why I think it's time to go over what the heck they are, how they work and well, why you need to actually be relying on them daily, not just using them. All right. I'm excited for today's show. I hope you are too. What's going on y'all? My name is Jordan Wilson, and welcome to Everyday AI. This is your daily live stream podcast and free daily newsletter helping everyday business leaders like you and me not just keep up with all these AI developments, but how we can make sense of them and put them to work for us to grow our companies and our careers. If that's you, like, hey.

Jordan Wilson [00:01:45]:
That's what I'm trying to do, Jordan. Welcome. This is your new home. So it starts here on the live stream podcast, unedited, unscripted, but if you wanna take it to the next level, that happens on our website, youreverydayai.com. Just go there. Sign up for the free daily newsletter. We're gonna be recapping today's episode as well as all the AI news you need to stay up to date. Also, you can go watch literally 600 plus of our backlog of episodes.

Jordan Wilson [00:02:10]:
Listen to them, read them all on our website. It's a free generative AI university, so make sure you go check it out. Enough chitchat. Let's get straight into chat GPT connectors, what they are, and why you literally need to be relying on them every time you open chat GBT. So in today's show, we're gonna detail the basics and also advanced functionality of chat GBT connectors. Talk about the similarities and differences between connectors and traditional rag or retrieval augmented generation. I'm gonna show you live. Yeah.

Jordan Wilson [00:02:39]:
We're gonna be getting to work live here on how they work and, how we use them, how I personally use them as well. And I'm gonna give you three or maybe more, real use cases. All right. So this is part of our new AI at work on Wednesday's segment. So essentially here's what we do Monday through Friday, Monday, we bring you the AI news that matters Tuesday. It's a hot take Tuesday. My take on something happening in the world of AI on Wednesdays, we put AI to work, show you a new tool, technique, mode that was released usually from the big companies and how we're doing something. And then usually Thursday and Fridays, we do different interviews or I might just do another show on something timely and relevant.

Jordan Wilson [00:03:18]:
So let's put it to work. Well, actually, if you really wanna put it to work, make sure you go repost this episode. Y'all, I've been having so much fun, because, yes, I know sometimes these episodes go a little long. I'm trying to keep them to thirty minutes, but they always creep up to, like, thirty five or forty minutes. But I have so much extra information in great material that never ends up making the cut for what I'm going over in the show. So I've been putting together a lot of these, kind of companion guides, and this one is amazing. There's so much additional information I just didn't have time to cover today. So make sure you go repost this show on LinkedIn, and I will send you, the chat GPT connectors cheat sheet.

Jordan Wilson [00:03:59]:
It is live. It is ready to go. So if you're listening on the podcast, I always put the link to the LinkedIn post. This actual livestream, go repost this, and I will share that with you. Also some other recent chat g b t tutorials and guides that are gonna help you. Go listen to episode five ninety nine, the five new overlooked chat g b t features you should be using but aren't, and then episode five eighty eight, chat g b t's updated canvas mode in g b t five, what's new and how to make it work for you. Alright. So let's talk about chat g b t connectors.

Jordan Wilson [00:04:31]:
So essentially think of these as secure bridges, and they link Chat GPT to, well, your data, specifically within, Google Drive, your Outlook, SharePoint, HubSpot, and more. We're gonna be going over all the different connectors as well as how, custom connectors in the model context protocol work. But, essentially, you might be figuring, like, why? Couldn't I just you know, while I'm using ChatGPT, couldn't I just go fetch information from the Internet or individually upload files? Well, yeah, you could. But this is a way to essentially bring your data, into ChatGPT. This is a version I like to say this is kind of like mini rag. This is a way that you can right? So instead of ChatGPT going and, searching for answers, aimlessly inside of its training data, which is just the entire Internet gobbled up, chewed up, and spit out right back at you, sometimes good answers, sometimes not. Yeah. You can manually go search for that file, or you can have CHET g p t go to a certain website, to hopefully give you better responses and a higher quality output or you can use connectors and connect your data.

Jordan Wilson [00:05:33]:
So I do have to always put this thing out here, big asterisk. Right? You know, make sure that you have permission within your company, to update all of this. It's it's very similar to, you know, if you're using, you know, Google Docs and there's a third party extension that you need to use, and it's gonna be able to view all of your information inside of your account. It's the same way. Right? So we're gonna be going over a little bit more on the security. That's essentially how it works, and what this gives you now is real time answers out of ChatGPT grounded in your business data, not just general Internet knowledge or just random training data, which may or may not be helpful. And this, I think, is one step to to transform ChatGPT into a true business assistance with citations you can trust. So here's why not using it can be a failure and why, when I say you should be relying on this, not just using it.

Jordan Wilson [00:06:27]:
You need to be relying on this because generic AI, right, can access your files, your information, your CRN, your calendar. So what this usually means, and I think in the 2023 and 2024 phase of chat GPT and other large language models, there was a lot of contact, copy and pasting. Right? When we talk about context engineering, right? Another hot and trendy term, just like rag was in 2023. This is essentially a shortcut to context engineering because it's gonna bring in automatically all of your business context once you, select these connectors. And right now, people business leaders are wasting countless times. Right? Especially power users. I'm shocked. Right? When companies, hire us to help them with chat GPT training, you know, front end AI strategy, I'm always shocked how few people, how few companies have their, connectors properly set up or even using them.

Jordan Wilson [00:07:30]:
Alright? Because what this means is they're just having to go search for files manually, or, you know, go and create files and bring them in to give Chetche PPT better context before you start. Because, you know, the more conversation, the more context that you share with Chetche PPT before you're going after a desired output, obviously, the higher quality, hopefully more accurate and relevant that that output is going to be the more that you work with it on the front end. So anyone that out of the, I don't know, 13 or 15,000 of you that took our prime prompt polish course, yeah, it's gonna be coming back soon. I swear. Right? You know this. Right? That's what we talk about in our refined queue method. You have to make the model smaller, smarter, and more specific for your business needs, and that's exactly what connectors do. But without connects without connectors, you're really just at risk, at higher risk for hallucinations or just giving generic outdated or incomplete answers.

Jordan Wilson [00:08:21]:
And the thing you have to think of as well, Chat GPT, just like any generative AI, tool, any large language models, it's generative. So you may run the same prompt 10 times, get 10 very different answers. Right? Sometimes it might automatically go on the internet. Sometimes it might pull old data from 2022, the exact same prompt. Right? That's why it's so important, to be using these connectors and relying on them, especially if you're using chat GPT for business. So here's a little bit how they work. So you log in once, using just standard authentication like you would authenticate, you know, any SaaS product, and then ChatGPT gains secure query access. So right now, there's not read write, permission within connectors.

Jordan Wilson [00:09:05]:
That's something to keep in mind, but you can do that via MCP or custom connectors, and then connectors inherit your existing app permissions. So there's no unauthorized data exposure, and then we're gonna go over all the different, supported connectors here in a second, but there's different ways that they work, which is important because previously, a lot of these connectors only worked with deep research, which when they first came out, I'm like, okay. Well, this is great. Right? But you might not wanna wait ten, twelve, fifteen minutes, to get something back. You might just want, you know, chat GBT to go through your Google Drive account, you know, maybe take two or three minutes and fetch different files, different information from different files, and that's it. So when they first came out, I don't think they were super useful, because at that point, right, if you had to wait ten to twelve minutes, it's like, okay. What's the difference then of just, you know, not doing a deep research and then manually doing that context engineering or manually uploading a couple of files, because then you can norm use the normal quote unquote chat mode where it's much more instantaneous. Right? Because in the end, we care about productivity.

Jordan Wilson [00:10:12]:
We care about getting higher quality outputs than the time that we're spending putting in, then we wouldn't or then we would be getting if we weren't using large language models. So, you know, a lot of times, I think early on, the ROI wasn't always there early on with connectors, but now that you can use them in normal chat mode they are. So the three different ways you can use them is there's chat search, which is an instant file or email lookup with clickable sources for verification. You have deep research. Right? So if you really wanna get in-depth, that can synthesize across multiple systems for complex multi source analysis, and then synced connectors as well. So this essentially pre indexed, drives and repositories for lightning fast responses. So in some instances, it's having to go out and kind of quote unquote, crawl your information, statically. In other instances with synced connectors, it's going to index all of that, and that's essentially where you get a version of rag.

Jordan Wilson [00:11:08]:
Right? It's kind of like, vectorized embeddings and, you know, your information's there, and it's gonna, kind of go against that, embedding before it goes to the large language model. So like I said, three different modes of operation. So if you need quick live lookups, in a lot of the connectors, you can use them in different modes and I'm gonna go over that here in a second. So if you just need something quick, just do the normal chat. If you need something more complex analysis across multiple files, across multiple connectors, you might wanna, activate deep research first, and then choose your connectors. And then if you need, synced files, you know, make sure that you use those options. All right, here we go. We're going to quickly go over the different connectors as well as the modes that they work in because, actually, didn't mention you can also use them in agent mode as well, which is really cool.

Jordan Wilson [00:12:08]:
And I think a lot of people don't know about that and don't even use that. All right. And if you caught our agent show, and if you've shared that show, I gave you the secret on how to, schedule agents as well, which a lot of people don't know about. That's why you gotta go share these, share these episodes. Y'all repost them on LinkedIn. I give you all the secrets that people don't know. Alright. So we're gonna go over these quick rapid fire.

Jordan Wilson [00:12:32]:
I'm gonna tell you the connector, what it can do, and kind of what mode it can work in. Here we go. So box, you can search and reference files, and this works in chat, deep research, and agent mode. Canva, this can find and fetch your Canva designs. It's not gonna, you know, again, it's not gonna write to them or design anything, but this is available in chat, deep research, and agent mode, GitHub. So GitHub is one of those that is synced, so it will sync your repo. So this lets you access repositories, issues, and pull requests, and it is required for some features such as codex if you're using Chachipedia's codex, and that works in chat deep research in agent mode. Again, that is synced.

Jordan Wilson [00:13:14]:
Another synced option is Gmail. This one banger. I'm so glad that this is no longer in deep research. I was, you know, getting impatient. So, with Gmail, you can find in reference emails from your inbox. It is live automatically. You don't even have to enable it once you, you you know, you don't have to check it once you enable it, which is great, and that works in chat, deep research, and agent mode. Similarly, Google Calendar, once you connect it, it is used automatically, and it works in chat, deep research, and agent mode.

Jordan Wilson [00:13:45]:
You have Google Drive. So Google Drive, you can search and reference files from your drive that works in chat deep research and agent mode, Dropbox. Similarly, find and access your stored files, chat deep research and agent mode, Google contacts. So this is something that it will recommend. You can let chat g b t recommend connecting to Google contacts when responding when appropriate. This lets you reference saved contact details. Right? If you're like, hey. What's billed from IT's phone number and email? Right? You can do that.

Jordan Wilson [00:14:18]:
This is right now only available in chat, not in any other modes. HubSpot. Here's low key. This one is good. I use HubSpot a ton for some other work, not necessarily for everyday AI. This lets you reference contacts, deals, and CRM data. And this works in chat deep research and agent mode, linear. So this lets you find and reference issues and projects if your company uses, you know, if you especially if you're a dev team, you probably use linear.

Jordan Wilson [00:14:46]:
That works in deep research and agent mode, so not chat mode, just deep research and agent mode. And then Notion. Notion is a newer one that was added a little more recently. This helps you search and reference your Notion pages, works in chat, deep research, and agent mode. Then we have the Microsoft lineup here. We have Outlook calendar. This just lets you look up events and availability. Unlike Google Calendar, this is not kind of enabled automatically, but this does work in deep research and agent mode.

Jordan Wilson [00:15:15]:
So, again, no quick ones, no quick chat queries, from Outlook. Same thing with Outlook email. So Outlook email and Outlook calendar, you don't get the quick responses. So Outlook email, same thing, search and reference your Outlook email only works in deep research and agent mode. But SharePoint, you get that instant, response. So SharePoint does work with chat deep research and agent in this that allows you to search and pull from shared sites in OneDrive as well. So OneDrive is not its own connector. It is actually working under SharePoint.

Jordan Wilson [00:15:50]:
And then last but not least, I think last. Yep. Teams. So teams lets you look up chats and messages, and luckily, this is available in all three modes, chat, deep research, and agent mode. Whew. I gotta take a sip after all that live stream audience. Let me know. Number one, have you used, these connectors so far? And if so, or maybe if not, after I just read them all, what one are you looking forward to the most? And if you have any questions, you know, go ahead and get them in.

Jordan Wilson [00:16:21]:
If I don't get to them here in the live stream, I will make sure to get to them, afterwards in the comments. Alright. Let's look live. What could go wrong? Nothing. Alright. Hey. Live stream audience. If you could let me know if you have my screen.

Jordan Wilson [00:16:39]:
So what I'm going to do here, I'm actually gonna get a a prompt going right away because I know this is gonna take a little while. Alright. And then I'm going to open up a new tab here, and I'm gonna show you around, I'm going to show you around connectors. Okay. So if you are brand new to chat g p t, it's it's going to look a little different depending on what plan that you're on. Interfaces change a little bit. I am on a pro account. Alright.

Jordan Wilson [00:17:11]:
I'm gonna resize my window here for our live stream audience. So hopefully you can see. So I'm using Chad GPT thinking mode. So you also technically have a little bit of fine tune control even when you're using just the chat mode, because you can use just the normal GPT five or you can use GPT five thinking. Connectors do not work, do not work with, Chad GPT five pro. And also, I don't know why it shows that they work with Thinking Mini, but I've never gotten them to work. So for the most part, you can use, Chat GPT five normal, or you can use Chat GPT five thinking, when you are working with connectors. Okay.

Jordan Wilson [00:17:51]:
So what you're gonna do, and again, your your interface might be a little different, but you're gonna click on the plus button, in where you would normally set a prompt, or, you can click the backspace, or not the back space, the, backslash button, and then it's going to bring up different options for, different modes or tools that you can use. From there, you're going to go to use connectors. Okay. And then by default, I already have some connectors connected. Right. And then you are going to choose your sources, but if you don't have any connected, all right, you're going to click that sources and then you're going to go in, click connect more. All right. And what I just said to you, all the different connectors, they're all going to be in this browse connectors.

Jordan Wilson [00:18:36]:
And this is also available if you go into settings, and then on the left hand side, go to connectors, but you can get there directly going the route that I just told you to. One thing to keep in mind, if you want to use model context protocol, that is Anthropic's advanced, kind of way that two different AI systems can talk to each other or AI systems like ChatGPT, Claude, Copilot, Gemini, can talk to the rest of the Internet the way that, websites have APIs. AI systems can talk to the rest of the web and other AIs using the MCP protocol or model context protocol. So if you want that, you can go into, developer mode and toggle that on. However, if you do that to use custom MCPs, your normal connectors are not gonna show up how they normally would. Just keep that in mind. Alright. So I'm not gonna go in and, you know, show you that.

Jordan Wilson [00:19:30]:
That's for another day. We'll do a a dedicated MCP show. Again, I ask you guys, I never know, but let me know MCP yes or MCP no. I sometimes I I say that and 50 people will reply. Sometimes one person will reply. So let me know MCP yes or MCP no if we should do a, a dedicated, episode on working with MCPs inside of chat GBT. It's It's a little niche, but I also think it's extremely powerful, but also even open AI said it's extremely dangerous. Alright.

Jordan Wilson [00:20:02]:
Anyways, if you are looking for MCPs, that's where they are. They're not gonna be under the browse connectors. Alright. And then from there, what you would do, let's say I want to connect my Outlook email. I don't really use Outlook. I use Gmail for the most part, but I would click Outlook email. Alright. And then I would click, this connect button right there, and then I would just continue to Outlook email.

Jordan Wilson [00:20:25]:
I'm not gonna click that because then, my email will be up there for everyone to see, and I'm already bad enough at responding to, your all very important emails. Alright. But then it's like there, like you would for any SSO connection, secure sign on, right, or single sign on. You just authorize it. You give it, you know, review access, and then you're ready to go. Right? It is that simple. Like I said, again, this isn't as powerful and robust and as as secure technically as as working with traditional rag, but you don't gotta spend 6 or 7 figures, and you don't gotta wait six to twelve months. Get it.

Jordan Wilson [00:21:01]:
Couple clicks of the button. Again, make sure check with your whoever is in charge of your data and security on your team that you have access to do that. If you are on a teams or enterprise plan, you're not even gonna be able to do this unless it's already allowed within an admin of your organization. So if you are in a teams or enterprise plan, you're like, why can't I do this? Jordan, you're saying all these things. You're wrong. It's not there. It's because your admin hasn't enabled that. Alright.

Jordan Wilson [00:21:26]:
But it's literally that simple. Alright. So I showed you how, to add a new connector. Alright. And then from there, you can select multiple connectors when you are doing a prompt. And the good thing is, ChatGPT obviously still retains all of its normal capabilities, when you are, putting in a prompt. So it still has all the, kind of agentic scaffolding that a model like GPT five or GPT five thinking should have. Right? It can think.

Jordan Wilson [00:21:57]:
It can still plan ahead. It can go back and forth between different connectors. It can, you know, start using, information from some of your connected integrations, then it can use advanced data analysis, start writing Python. It can then go, you know, jump and browse the web for something, then it can go back to one of your other connectors. Again, can't write, you you know, to your files inside Google Drive or it can't respond to emails just yet. Although, I do know that that will be coming at some point soon. But again, it can read, view, and share information across connectors, as well as it's still you're still working with that big brain. Right? My model of choice is normally, g p t five thinking or g p t five pro.

Jordan Wilson [00:22:38]:
Unfortunately, connectors don't work with pro. So in this case, I'm using g p t five thinking. And again, keep in mind that you still have all the capabilities. I think a lot of times when I see people, talking about connectors, they're just using it as like a one trick pony. And I'm like, well, why are you only doing it like that? You should be, you know, having, Chatt GPT pull and collaborate with the information between multiple connectors and then go help you get some work done. Alright. So I did start right before I started. I started a prompt, because I figure it's going to take a couple of minutes.

Jordan Wilson [00:23:15]:
So, let's go ahead and, check-in on that. Oh, cool. It's actually done. So here's what I said. Alright. So, this let's see how long this took. It took about four minutes and, fifty three seconds. So here's what I said.

Jordan Wilson [00:23:31]:
I said and I intentionally tried to throw Chad JB T off, and, we'll see if it, took the bait or if it did things correctly. I said, please check my Google calendar and Gmail connected here. So again, I'm going to this chat and I can show you what's connected. My Gmail is auto. That connector is searched automatically because I already have it connected. My Google Calendar is auto, and then I had Google or sorry, I had Canva selected on Google Drive selected on. So again, you can toggle these connectors on or off. You don't just like let's say you connect 10 of them.

Jordan Wilson [00:24:05]:
You don't wanna have all 10 of those toggles toggled on, especially if you are giving it a complex prompt because Chat GPT, again, it's generative, and it might decide, oh, I need to look in your CRM for some of this information. Right? You're asking me about an email in your calendar. I should just double check your CRM, and then all of a sudden you're waiting way too long. So make sure don't just keep all of those, all of those integrations toggled on because sometimes you should still call out and say, go use this connector, but sometimes you don't have to, and it's smart enough to know. But there's a flip side to that. You might be waiting way longer than you may think, or you may want, if you leave too many of those connectors toggled on. Alright. So let's quickly get to this use case, my example, and this is one way that I'm using connectors all the time.

Jordan Wilson [00:24:50]:
So I said, please check my Google Calendar in Gmail connected here. I have a podcast recording next week with WWT. I forgot who it's with. Research them. There might be duplicates on my calendar, so check it all. Tell me the details from that invite, then find the corresponding email in my Gmail. After that, please carefully look through my Canva account and find relevant docs to the show discussed. Essentially, I'm going on, I had WWT.

Jordan Wilson [00:25:18]:
They're they're they're a huge tech company, one of the biggest tech companies in the world. I had their CEO on my podcast, like, two years ago. I'm going on their fantastic podcast, here pretty soon. And this is what I normally I waste so much time. Right? Because I have to open my calendar. I have to open my email. Usually, there's multiple threads within my email. Then I'm having to do some some research.

Jordan Wilson [00:25:39]:
Right? If if it's like, oh, yeah. Yeah. Yeah. I'm gonna talk about, you know, I don't know, the the ancient washing. Right? Oh, that was in my Canva document. Right? I I, a lot of my, podcast well, every single podcast, I always have slides, and I put some notes in there. Right. It's still technically unscripted.

Jordan Wilson [00:25:58]:
I'm still riffing off the top, but I have a lot of good information that just lives in my Canva. And it's hard to pull things out of there. Right? I literally have 600, essentially PDFs that I've created with a ton of great information. So what I would normally do before connectors and before kind of Chativity is I would have to open multiple email threads. I'm gonna get distracted. I'm gonna see an important email that I forgot to reply to. I'm gonna have to go reply to that. Then I'm gonna open my calendar.

Jordan Wilson [00:26:24]:
Oh, crap. I have this, you know, I have this, calendar invite that I gotta go check. Right? It's so easy to get distracted, especially if you're a small business owner like me and you're wearing 82 different hats. It is so difficult. Right? And then I would have to go, you know, do a little bit of research on the show, pull some of my information, start compiling it, all this. Right? To normally, before AI and before Chat GPT, before connectors, this little project here would take me three to six hours. Right? It would take me a long time. Go through, read all these emails, take notes, go do some research, find my old information of things that they wanna talk about.

Jordan Wilson [00:26:59]:
They told me some things they wanna talk about. I know I've covered it all. I gotta go find all the information. I gotta start copying and pasting all this. Right? Nope. Not anymore. I use connectors. Alright.

Jordan Wilson [00:27:07]:
Let me keep going. So I said, then find the corresponding email in my Gmail. After that, please look carefully through my Canva account and find relevant docs to the show discussed. There's a ton of Canva docs, so please go step by step and search deeply. I said in your reply back, do not include email addresses. I didn't want, you know, anyone's email address from WWT to be exposed here on the live stream. And then I said, just use, their names and other details, to accommodate. And then I have something in there about my custom instructions, to get, going through a little more quickly.

Jordan Wilson [00:27:37]:
And then I said, in this task, please reply back with specific bullet points showing the info from my calendar, the email recap based on that calendar event, and then relevant info from my Canva documents. And I'm saying, give me actual specifics, not just like, hey. In this Canva document, you talk about blah blah blah. And then I said, also, additionally, research the web about topics not covered in my Canva docs, but that are discussed in that email. And then I just said reply back with easy to read formatting. And for whatever reason, g b t five thinking just doesn't can't format. So I'm actually gonna quickly switch over, to g b t five, auto, to, essentially just reformat this. But, let's look through and see what happened.

Jordan Wilson [00:28:21]:
K. So now I'm gonna click on the thought, and I can kind of show you, what's happening. Again, a lot of this is trying to avoid, some of my custom instructions, and it's overriding them. Alright. But essentially, what's happening here is it's going through my calendar, and it finds out and here's the trick. I said next week. It's actually not next week. It's tomorrow.

Jordan Wilson [00:28:39]:
Alright. And it looks like, Chat GPT and the connectors was smart enough. It found the the WWT references in my email and on my calendar, and it's like, yo, Jordan, it's not next week. It's actually tomorrow. So went through and it found it searched in my calendar for anything WWT. It found it. It looks like that is, happening, tomorrow. There we go.

Jordan Wilson [00:29:03]:
It's the people who it's with, and then it's searching, for that calendar event and some of those keywords, the people I'm meeting with in my Gmail. Alright? Then it found 10 different emails. We weren't collaborating on all of those tens. I've just emailed, some of these people before dating back a couple of years ago. Right. So it's going through, it's looking at all of those emails to surface just the relevant information that aligns with the calendar invite. Alright? And then from there, it says, I'll now search the Canva documents with multiple queries, including the terms. Right? So it's searching terms like WWT podcast, worldwide technology, AIPG podcast.

Jordan Wilson [00:29:43]:
Right? Some of the keywords that it found in the, in the email, and then it's going through. It's doing, some some more, some more, searches. It's searching now. After all that, it went through all my documents. Now it's finding out information that I didn't have, in my Canva docs to go find. Right? I told it, hey. Who who is this podcast with? Alright. I know who it's with.

Jordan Wilson [00:30:11]:
But I I I well, I actually, I haven't met everyone that's gonna be on the call, so it is going to do some more additional research for me. Alright? So it's it's searching on their website. It's finding information about these people that I'm meeting with. Looks like it's going in there and looking at, their podcast. It's It's great podcast called, by the way, give it a plug. It's, it's called WWTs AI proving ground. Alright? So it's looking at, that podcast, who they partner with, what they normally cover, the topics that we discussed. Right? You see all of this information.

Jordan Wilson [00:30:43]:
It is going out synthesizing information from all of my business context, then it is going through, it is researching based on my business context and what I told it to do. It's synthesizing all this information and then personalizing it exactly how I need it. Alright. So then I can go down and bam. There we go. Alright. So here it is, the calendar info. It found the correct time.

Jordan Wilson [00:31:07]:
It gave me the details from my calendar. Then here's the email recap based on the calendar. There we go. Some of the things that we're gonna be covering, we're gonna be, we wanna talk about my roasting of the MIT study. Can't wait. It's gonna be a fun one, so make sure you tune in to the, WWT AI proving grounds, podcast. Alright. There we go.

Jordan Wilson [00:31:26]:
And then it's bringing in relevant info from my Canva documents. Perfect. And, again, this is where site, citing in sources matter. So it's pulling in, and you'll see this the Canva integration is actually really good because I do a bad job of naming my files. This one is called copy of copy of copy of copy of copy of copy of copilot free. Right? I should probably start naming them a little bit better, but that just shows you how good of a job. And I wasn't even in the deep research mode. I was technically in the chat mode, but I was using g p t five thinking, so it did a really good job.

Jordan Wilson [00:32:03]:
You know, it's it's it's a model with agentic capabilities. It did a good job of digging deep as I told it to. And then it finds, even though I'm not naming my files correctly, it's finding context inside of those Canva documents. Right? And then I can, click on these, right, if I wanna double check something. So it looks like, from Microsoft build. Alright? So it looks like something, in there is related, to what we may be covering on the podcast, but I don't even have to go in and read it if I don't want to because it broke down everything that we talked about. Right? So, looks like we'll probably be talking about multi agentic orchestration and Copilot studio, a to a protocol, MCP protocol, support across GitHub, Azure Dynamics, Windows 11. Right? So it's all right there.

Jordan Wilson [00:32:49]:
Right? I spent so much time putting all this information together over the years. So I don't have to spend three to six hours going to prepare for it. It's all right here. Yeah. I did mention g p t five thinking not great at, not great at, you know, having nicely formatted, information there. But at the at the bottom, all of this information is cited at the bottom, so I can go click and check everything. Fantastic. And then I just ran another prompt that just said, you know, format this better.

Jordan Wilson [00:33:23]:
And then I used, GPT five auto, and there we go. So that's one quick example, but you'll see what I did there. I'm using in that instance, I use three different connectors. All right. Because again, if you really want to get the, the, the true utility and the true time savings, the true ROI on this Don't don't think of it as just like uploading a file. Right? Think of those instances like, like myself, maybe where you personally struggle, to, to, to focus, to concentrate things that you just find difficult, Right? Maybe your SharePoint, you just stink finding information across there, but Chat GPT is great. Or when you go into HubSpot, right, you get lost inside looking for things. I do.

Jordan Wilson [00:34:05]:
Right? The chat gbt connector is fantastic for HubSpot. Right? And you can go and say, hey, check, you know, check my HubSpot, check, check my emails, you know, check my team's messages and tell me what I should be focusing on today. That right there, you can run that every single day, and it's probably how you might be. Right? Let's just say, you know, you use HubSpot and, Teams and, Gmail. Right? That could be how you spend most of your first two hours of the day, and you can get a good kickstart in ten or fifteen minutes and keep refining and improving your prompt day by day. Again, think of balancing between multiple connectors and then having a very smart, assistant, an assistant that's smarter than you in g p t five. Just go ahead and use g p t five thinking for me. It's worth the extra, you know, four minutes there.

Jordan Wilson [00:34:53]:
A lot of times, I'll just run multiple of these prompts. Go go get my coffee, upstairs, come downstairs, and I'm ready to go. Right. That's how you need to be thinking of it. Alright. So there we go. We have a demo that didn't break. Alright.

Jordan Wilson [00:35:09]:
Live stream audience. What do you think? Impressive? Yawn. Are you ready to put AI to work on this Wednesday? All right. Now let's very quickly, go over some differences between rag, and connectors, and then talk a little bit, about some use cases. We're gonna go quick here. So buckle up y'all. Buckle up buttercup. Here we go.

Jordan Wilson [00:35:29]:
So, what's the difference? Is this rag? Not really. Can it, substitute for rag, traditional large language model rag in some use cases? Sure. Alright. So both retrieval, ex, so the the similarities between connectors and traditional rag, well, they both retrieve external data first, then they generate answers, minimizing hallucinations. That's number one. So traditional rag obviously requires custom pipelines and beddings in some database engineering. Connectors, plug and play hardly no setup, couple clicks. You're ready to go in there maintained and updated automatically by OpenAI.

Jordan Wilson [00:36:13]:
Some of the big differences. Well, you saw it connectors depending on if you're on a team plan, enterprise plan, pro plan teams, right? But I mean, you're looking at anywhere from 20 to $200 a month per user. Actually, probably a $100 or or, yeah, $200 a month for the pro plan per user, and that's instant deployment. No technical overhead. And then with traditional rag, very different. Right? Yeah. It's it's getting much easier, to have actual, you know, rag instances out the door, working with different large language models. It's much easier than it used to be, but still traditional rag projects still might cost anywhere from 10,000 to 500,000 or more.

Jordan Wilson [00:36:57]:
But, obviously, higher accuracy and lower per query cost at scale. Right? But the best fit, I think connectors for most businesses will work. Right? And I think RAG for highly specialized or high value or high volume use cases. Right? If if if you're a Fortune 100 company, and whatever that you're trying to automate is the backbone of your business, you're probably gonna be way better off with RAG, especially because right now connectors don't have read write. You know, default connectors just have read access. Right? RAG can be a little different. Now let's talk a little bit about MCP and how you can actually really expand capabilities with custom connectors via MCP. So, Infropix model context protocol lets developers build connectors for proprietary systems.

Jordan Wilson [00:37:50]:
So right now, you know, I I rattled off about a dozen 15 different connectors that are supported and created by OpenAI, but you can literally do this with anything. Alright? And MCP is essentially a USB c for AI. It lets AI talk to other AI and different large language models talk to essentially a bunch of Internet websites. And this does also support future right actions. So updating CRMs, ticketing tools, or calendars with approval. Alright. Let's go over three, what I think are pretty cool use cases for chat GPD connector. So number one, preparing for a high stakes client meeting.

Jordan Wilson [00:38:24]:
To kinda give you an example, a meeting I'm prepping for, it's not high stakes. It's gonna be a fun conversation, but think, what are those? And, and, and when you think about use cases, I rattled off all those connectors, write down the ones that you use and then start tracking. How are you wasting your time in there? Go in your internet browsing history and see, oh my gosh, I spent four hours today in HubSpot. Oh my gosh. I spent, you know, two and a half hours in, you know, teams in SharePoint today. Why? Right? You have to almost manually audit yourself to start building these use cases. So use case one, preparing for a high stakes client meeting. So without connectors, you're gonna have to manually search HubSpot for client history, Gmail for recent emails, and then Google Calendar for up, for upcoming meetings.

Jordan Wilson [00:39:08]:
That could easily take you thirty minutes to a couple of hours. So with connectors, enable HubSpot, Gmail, Google Calendar, and then ask. Summarize this client status using CRM history, latest emails, and upcoming meetings, and then go ahead and have it do some, some, you know, web research for you as well. And then the outcome, well, consolidated verifiable briefing across three different systems, probably saving more than 90% of the time if you were doing it manually without AI slash chat. Use case two, answering HR or compliance policy questions. Don't we all love my HR people? Don't you just love answering the same questions over and over and over? What if you could empower people, to just get those answers themselves? Alright. So without connectors, you're gonna have to dig through your SharePoint HR folders, open PDF policies, read them, summarize them, and maybe ask HR staff. It could take a lot.

Jordan Wilson [00:39:59]:
One simple query could easily take fifteen minutes, an hour, maybe more. With connectors, go ahead, connect SharePoint, OneDrive, and Outlook, and ask, what is our PTO carryover policy and who approves exceptions? Thirty seconds. You're gonna get instant cited answered, pull from synced HR handbooks, flagged Outlook memos, and official documents eliminated, wasted time searching. Alright. Use case number three, generating cross functional reports. Here's a great one. Without connectors, this is a headache. So you gotta export Excel spreadsheets, compare data in Google Drive, then manually draft a summary in Word.

Jordan Wilson [00:40:34]:
So with connectors, you can connect Google Drive, OneDrive, and SharePoint, and ask. Summarize quarter three budget variance across finance spreadsheets and draft and executive summary. Couple minutes versus couple of hours. Then you have a reliable grounded report combining spreadsheet and documentation from multiple repositories, cutting reporting time by probably more than 90%. Alright. So let's wrap up here with the question of why. Why should business leaders be using chat TPT connectors now? Well, if you're not already convinced from what I already went over, that's fine. I'm not here to win you over.

Jordan Wilson [00:41:16]:
OpenAI is not paying me a dime to say any of this, but I want you to get more out of AI. And I think chat GPT connectors again, as long as you have access to do it, it is a no brainer. You need to rely on them, not just use them, rely on them because not only is it instantly gonna save you more time trying to find the files that you may be updating, manually, you know, going through, you know, kind of proper quote unquote context engineering process. But you're cutting down hallucinations and you're increasing the accuracy and richness of every single response when you use connectors. So you're gonna get measurable productivity gains, hours saved weekly across sales, HR, finance, and ops, And connectors unify silo data. That's the biggest thing into a single reliable source of truth in chat GBT. That's the thing. Your data does not have to live in silos.

Jordan Wilson [00:42:11]:
Right? If you use, you know, HubSpot, Gmail, and, Notion. Right? Those systems by default can't talk to each other. ChatGPT can make them talk to each other, and then you can create new business value out of that, out of those relationships. And then like I said, for most organization connectors deliver, I don't know, 80% of the benefits of traditional retrieval augmented generation at 2% of the cost and 1% of the time required. So again, this is not going to, replace. Right? If you're a fortune 100 company, this is not going to, replace an intricate and robust rag setup. But in many instances, you might not need it. Right? Like I said, I think you get about 80% of rags benefits at 2% of the cost in 1% of the time required.

Jordan Wilson [00:43:04]:
And if that's not putting AI to work for you on Wednesdays, I don't know what is. Alright. I hope this was helpful. We went over how connectors work. They are a secure bridge, right between chat GBT's default responses, which can absolutely stink and be riddled with hallucinations. They bridge your data, so you're not gonna be getting, these generic and maybe incorrect answers as frequently when you are using connectors and using them in the right way to share context and to create a smaller, more specific version of chat GPT based on your company's data. And like I said, you shouldn't just be using them. You should be relying on them.

Jordan Wilson [00:43:46]:
If this episode was helpful, please find the LinkedIn. Maybe you're watching this on LinkedIn. Maybe you're listening on the podcast. Go ahead. Check the show notes. If you're listening on the podcast, we're gonna have it on our newsletter everywhere else. Go repost this, LinkedIn live stream, and I'm gonna send you the chat GPT connectors cheat sheet because here we are. Obviously, I went forty four minutes, but I could've went for another three hours because there are so many great use cases, so many great tips and tricks on how to properly use connectors, especially stacking these connectors with the power of GPT five and especially GPT five thinking.

Jordan Wilson [00:44:24]:
So if you want the cheat sheet, I'm telling you, I put a ton of work on this one. You're gonna want it. So make sure you go repost this episode. Sometimes I'll send it to you within a hour or so. Might take me a couple of day depending, on on when you repost it. But just go ahead and repost it. Hit me with a message if you don't get it right away, and I'll send it to you. Trust me.

Jordan Wilson [00:44:42]:
You're gonna get a ton of value from that. So if you haven't already, please go to your everydayai.com. Sign up for the free daily newsletter. Thank you for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.