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We've tested the latest ChatGPT upgrades, features, and capabilities for 2026. We tested seven different scenarios to help you utilize them all.
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How to Actually Use ChatGPT in 2026: Pinpoint Strategies for Business Impact
Most business leaders are aware that AI adoption is accelerating, but many are stuck using ChatGPT with outdated methods or missing key features. This guide specifically breaks down the seven crucial rules highlighted in a recent analysis, clarifying how to maximize business efficiency, streamline collaboration, and generate competitive advantage with ChatGPT in 2026.
ChatGPT Free vs. Paid Plans: Understanding the Value for Business Workflows
Despite over 900 million weekly users, the vast majority are relying on ChatGPT’s free version—a decision that limits business outcomes. The free tier operates on the GPT-5.2 Instant model, which lacks advanced reasoning, planning, and high-level data handling. According to recent third-party benchmarks, this version ranks 25th for performance, while the “thinking” GPT-5.2 Pro model—available only through subscription—is tied for first with Gemini 3 Pro.
Investing $20/month per user grants access to superior outputs, higher-quality reasoning, and substantial time savings—often recouped in just a few prompts per month. For organizations, this cost is negligible compared to the hours saved on analysis, project work, and content generation. Staying on the free plan is demonstrably inefficient for any company aiming to compete in high-output environments.
Optimizing Results with "Thinking" Models for Decision Makers
Lazy or rushed usage habits—like defaulting to Instant or Auto models—result in lower quality outputs and missed strategic insights. The GPT-5.2 Pro (thinking) model not only processes more complex instructions, but also produces outcomes that align more closely with expert decision-making processes.
Current benchmarks show the far-reaching superiority of thinking models. Tolerating a modest wait time delivers a magnitude leap in quality: better summarization, analysis, and actionable recommendations. For decision makers, instructing teams to wait sixty seconds for a thinking model rather than ten seconds for an instant model translates directly into greater business intelligence and less rework.
The Art of Context Switching within ChatGPT for Enhanced Productivity
One of ChatGPT’s enterprise strengths is seamless “context switching” between models, modes, and custom GPTs—without losing conversation history or project memory. While alternatives like Gemini or Claude require users to restart chats (and lose valuable context) when switching modes, ChatGPT preserves project momentum and data lineage.
Business users can move from lightweight Q&A to “deep research” modes and leverage multiple GPTs for specialized outcomes within the same workspace—dramatically improving the fluidity of team-based problem-solving, research, and reporting.
Leveraging Projects and Custom GPTs for Sustainable Business Knowledge
Rather than defaulting to new, ad-hoc chats, effective teams organize work in Projects and build Custom GPTs tied to business data. Projects offer a hierarchy similar to folder structures, but with added benefits: persistent custom instructions, integrated file storage, and—crucially—project-specific memory sharable across users.
Custom GPTs allow for reusable, no-code business solutions. For example, a dashboard generator GPT can automatically visualize research, KPIs, or aggregated themes from market predictions. Investing time up-front to structure Projects and Custom GPTs yields compounding returns in efficiency, collaboration, and knowledge transfer.
Redefining Business Data Integration: Connectors Now "Apps"
ChatGPT’s integration with business data sources is being upgraded from “Connectors” to “Apps.” Businesses can connect Google Drive, SharePoint, email, CRM, and more, turning ChatGPT into an interactive knowledge hub.
The shift to Apps unifies features like file search, deep research, and data syncing, giving every team member access to dynamic, searchable organizational knowledge—without requiring expertise in vector databases or RAG techniques. The ease of connecting key workflows and data sources to ChatGPT ensures no critical information remains siloed.
Maximizing Teamwide Collaboration with ChatGPT Enterprise Features
92% of Fortune 500 companies use OpenAI technology, and enterprise adoption is accelerating. ChatGPT for Teams/Business/Enterprise enables organization-wide knowledge sharing, structured projects, group memory, and no-code custom tools—all inside an AI-operating system.
Team features like shared projects and project-specific memory minimize repeat work and promote insight sharing. In 2025, enterprise organizations saw an 8x increase in ChatGPT usage and a 19x jump in structured workflows—a testament to the platform’s scalability for business collaboration.
Chain-of-Thought Summaries: Ensuring Output Quality and Strategic Clarity
Chain-of-thought summaries, now accessible with each deep research or thinking-model output, give transparent insight into the AI’s reasoning process. Leaders can audit each step, verify decisions, and spot context gaps or logic errors before action is taken.
This visibility is invaluable in regulated industries, complex strategic planning, or high-stakes client deliverables. Reviewing the AI’s full problem-solving chain enables teams to correct, refine, and trust results—turning AI from a black box into a proactive collaborator.
Conclusion: Pinpointed ChatGPT Best Practices for Business Leadership
Maximizing ChatGPT in professional environments relies on seven actionable rules:
Upgrade to the paid plan for best-in-class performance.
Prioritize thinking models for all substantial tasks.
Leverage seamless context switching to avoid data loss.
Build and organize with Projects and custom GPTs.
Integrate business data using updated Apps.
Deploy ChatGPT for team collaboration, not just individual use.
Audit and enhance outputs with chain-of-thought summaries.
Applying these strategies positions any business to extract measurable value—saving hours, reducing rework, and compounding insights. Those who implement these methods will not only keep up with the pace of AI innovation in 2026, but will also create systems that continuously improve knowledge sharing, productivity, and business outcomes.
For business leaders ready to see practical demonstrations, structured dashboards, and AI-driven workflows, additional resources and walkthroughs are available at AI-focused knowledge hubs and peer communities.
Topics Covered in This Episode:
- ChatGPT Free vs Paid Model Breakdown
- Importance of Using Thinking Models
- Context Switching in ChatGPT Workflows
- Projects and Custom GPTs for Productivity
- Apps and Connectors for Business Data Integration
- ChatGPT for Teams, Business, and Enterprise
- Leveraging Chain of Thought Summaries
- Live Demo: Building Dashboards with ChatGPT
Episode Transcript
Jordan Wilson [00:00:16]:
There's nearly 900,000,000 people using ChatGPT each week, but I've gotta be honest. Very few people have a clue what they're doing. It's like being given keys to a Ferrari, but you just use it to keep dry and cover up when it rains. It's not how you should be using it, especially for teams, because ChatGPT at work is an actual competitive cheat code. But the problem is it's nearly impossible to keep up with the updates, the changes, the model releases, and all the new features that come and go. And I'm not trying to be mean, but you're still kind of using Chegg PBT like it's November 2022. Don't worry. We're gonna change that on today's show because this AI thing is all I do, and I'm fairly okay at this Chat GPT thing.
Jordan Wilson [00:01:10]:
I did even get a DM from someone in leadership at OpenAI who said that there aren't many people in the world who know chat g p t better than me. Well, I'm gonna share it all with you. So stick with me for the next twenty five ish minutes as I lay it all out for you in today's episode in our first AI at Work Wednesday series of 2026. So we're gonna be going over how to actually use Chegg GPT in 2026 and the seven rules to turn you into a power user. Alright? Let's dive into it. If you're new here, welcome. My name is Jordan Wilson, and this is Everyday AI. This is your daily livestream podcast and free daily newsletter helping everyday business leaders like you and me make sense of all the AI updates and chaos and just grab the important insights to grow our companies and our careers.
Jordan Wilson [00:02:02]:
So it starts here with the unedited, unscripted livestream podcast. But if you wanna be the smartest person in AI at your company, that happens at our website, youreverydayai.com. There, make sure to go sign up for the free daily newsletter. We're gonna be recapping the highlights from today's show as well as all of the other AI news. It's all gonna be in the newsletter. Alright. And I'm looking at my watch now. It's gonna be any minute.
Jordan Wilson [00:02:26]:
We're either launching our free AI inner circle community either today or tomorrow. Alright. But, I'm I'm not kidding. I checked my email. We have a couple thousand people that are waiting to get in. I kid you not. So if you wanna move to the front of the line and get access, including our free course. So if you find today's show helpful, you're gonna find the free it's about a almost a two hour course.
Jordan Wilson [00:02:53]:
You can take it at your own pace, prime prop polish. So just repost today's show on LinkedIn. I'll get you to the front of the line. We're probably gonna let in, I don't know, about 50 or so people, a day. So if you do wanna get, early access, just repost today's show. Alright. Enough chitchat. Let me give you these seven rules to be a power user, and then we're gonna dive into each of these.
Jordan Wilson [00:03:16]:
And then because it is AI at work on Wednesdays, we're gonna go live, live ish. Alright. So what could go wrong? Alright. Ready? Rule number one, you do not use the free version of Chad g b t. Number two, you should almost always use thinking models. Number three, you should be context switching between the right model mode or GPTs. Number four, you should use more custom GPTs and projects than you think. Number five, connectors are now apps, but they're still connected and you should still be using them.
Jordan Wilson [00:03:52]:
I'll explain that one later. Number six, chat g p t is best for teams. Everything from project memory, shared g p t's, and even group chats. And number seven, kind of advanced, but kind of not. You should be leveraging chain of thought summaries as your secret weapon. Whew. All right. Let's start at the top.
Jordan Wilson [00:04:17]:
Do not use the free version of chat gbt. That is number one. Alright? Here's why. And, hopefully, those people listening, over there at OpenAI don't get mad at me for saying this. It's not the best model. Right? The version of, GPT that you get on the free plan is not what you think. Right? When they first announced GPT five, there is this new thing called the auto, you know, the auto model router and, you know, even the free people got it. So you got this new level of intelligence.
Jordan Wilson [00:04:54]:
Right? And I did go over this a little bit more, on yesterday's show. So make sure if you haven't listened to that, go click the back button when you're done here and listen to that one. But, you know, essentially, there's two different types of AI models. Right? At least today. There's models that can think and reason and plan, and then there's those that can't really. Alright. So, OpenAI just this was an under the radar update. Not a lot of people know about this.
Jordan Wilson [00:05:21]:
People still assume, no. I can use the free model, because if I ask it a tough question, it's gonna use that model router and send me to a really good model. No. Okay. So there is a, technically a newer version of GPT five two. That is the latest version of Chad GPT. But if you are on the free plan, it is using a model called GPT five two instant, which by all comparisons is not very good. Right? It's not, a a type of model that is gonna be able to handle a lot of your data.
Jordan Wilson [00:05:56]:
It's it's not gonna be able to, do a lot of high level thinking, reasoning, planning. The the outputs are going to be bad. Alright. And most numbers suggest that more than 95% of, ChattGPT's, you know, 900,000,000 users are on the free plan. You shouldn't be. Right? OpenAI is not paying me to say this. Right? And I feel the same way, about Google Gemini, about anthropic Claude, about Copilot. You shouldn't be on a free plan.
Jordan Wilson [00:06:26]:
I understand. Right? Economy's tough. Getting a job is tough. Yes. It is criminally cheap. Any, right, any of those plans for $20 a month, you can go out there and compete with anyone doing anything, right? Whether you're trying to get a job, whether you're trying to get a promotion, whether you're trying to push your company forward, you can't do that on the free version of anything period. The fact that you can still get this level of intelligence for $20 is mind boggling to me that people don't pay that. Right? One time, me and my wife went and got a coffee, and it was more than $20.
Jordan Wilson [00:07:08]:
It was some fancy coffee. It wasn't even that good. Right? But think like, again, even if money is tight, right, or or maybe you're making the decision for your company, single prompts to me have saved me dozens of hours, single prompts, right? Not just the time that you can save. Right. You have to be able to quantify and put a price tag on that. Your time is valuable. Right, because you might say, oh, I don't I don't have $20 a month. Okay.
Jordan Wilson [00:07:41]:
Well, there's always the saying. Money can buy you anything, but it can't buy you time. Well, yeah, it can't. If you're using large language models, my gosh. Right? So what could you do with another couple hours a week? Well, you could go, you know, applied more jobs if you are job hunting, right, or whatever the case may be. So rule number one, do not use the free version of chat g b t. And rule number two is kind of related to that because rule number two, you should almost always be using the thinking models. I get it.
Jordan Wilson [00:08:11]:
You and by you, we as a society, we as humans, for the most part, are lazy and impatient. And it's actually weird. Like, as these AI models get more and more capable, as they get more, robust, as they get smarter, as their agentic capabilities increase, it's almost like the default for us humans is to do the opposite. Right. Is to kick back more, be even lazier. Right. But we just want things faster. So sometimes I see people just using like the instant model, right.
Jordan Wilson [00:08:48]:
Or just using the, kind of the the default, auto. Right? No. Don't do that. You should almost always be using thinking models. Alright. So, our livestream audience can see this. And if you ever do wanna see the video version of this, you can always go to our website. Alright? Youreverydayai.com.
Jordan Wilson [00:09:10]:
The video versions are there. It's a library. You can go listen, watch, read everything on the website for free. It's literally a university of free unbiased information on AI. Alright. But I do have a screenshot from artificial, analysis. It's one of the best unbiased third party benchmarking sites. Uses a series of different benchmarks from other companies.
Jordan Wilson [00:09:32]:
And they run these different tests and, put their results up. You'll see the free version, g p t five two, the basic version. Well, not just the free version, but the basic version of g p t five two. It is the twenty fifth best model. Let me repeat that. The twenty fifth best model. The high thinking version is the first. It's tied for first with Gemini three pro.
Jordan Wilson [00:10:03]:
Let me repeat that. Gemini three pro in the, the thinking, the high thinking version of GPT five two are in first place. The version that you're probably using is twenty fifth. Right? I don't know. You need to be patient. Right? Sometimes people just want something in ten seconds or less, and they don't wanna wait, you know, a minute, two minutes. Oh my gosh. Okay? I don't know.
Jordan Wilson [00:10:30]:
Maybe I should take my own advice. Right. It's like, oh, what do you do during that one or two minutes? Well, I have way too many subscriptions. I think I have three or four paid plans to, OpenAI. I have at least two paid plans to Google Gemini. I've I've a max plan to to Claude. So, you know, normally, when I'm waiting for one, I'm working on two or three others or having an agent go, read two or three others, and I'm reading the agent's recap of two or three other large language models. Right? But I don't know.
Jordan Wilson [00:10:57]:
While you're waiting, I don't know. Do some push ups. Send someone a text. Right? I don't know. Don't doom scroll. Don't go on Sora. Right. But the the difference and this is not just some, you know, dorky benchmark.
Jordan Wilson [00:11:14]:
This is facts. This is stacks. This is science. This is outputs. Alright? This is your career. This is your company. This is your department. That choice that you probably aren't making.
Jordan Wilson [00:11:30]:
Right? A lot of people don't think. They just use whatever's there by default and you assume it's good. You see, oh, it says g p you know, it says, you know, GPT five on it, so it's good. Or, you know, it says GPT five two, so we're good. No. No. We're not. Alright.
Jordan Wilson [00:11:44]:
Don't use it. Alright. Rule number three. Alright. Here's one of my little, phrases I used to use a lot, context switching. This is actually a huge advantage that chat GBT has that other large language models don't. Alright? Without getting too deep into, the context window. Right? Again, just repost this show, get you in the front of the line, and, we'll we'll we'll go deep into the context window and why that's important.
Jordan Wilson [00:12:13]:
But more or less, right, with some other large language models, even Google Gemini, even Claude, Copilot. Right? When you're switching from model to model or maybe when you're switching different modes or different features, it stops. Right? That's one thing. I love Gemini three, the the the deep research from Gemini three. It is so freaking good. Right? Well, one thing I usually like to do is context switch. So after I do a deep research, I like to continue chatting, but not in deep research. But you can't do that in Google Gemini.
Jordan Wilson [00:12:49]:
Same thing with, with with Claude. Right? When you switch if you wanna switch over from a more powerful model, maybe, you know, Opus 4.5 with reasoning and, hey, I need to kick it over to Sonnet. Can't do it. Gotta start over, and you lose all the context. You lose the work. So it's like you start with a blank document. That's bad. So this is one of the biggest cheat codes I'd say that most average users skip over is not taking advantage of a built in feature that a lot of people don't know.
Jordan Wilson [00:13:16]:
And it's not just between the the models. It's with the modes as well. Right? We're not gonna get too much into that in today's episode. That's not it's a little more advanced, but I wanted to keep this one a little shorter. But even if you're using the different modes. Right? GPTs, Canvas mode, study and learn. Right? Whatever mode you're using, you can keep it going, using the different modes and not have to start over. Right? You know, not having to re explain yourself, not having to copy and paste anything.
Jordan Wilson [00:13:47]:
Chat JPT doesn't skip a beat when you change a model or mode. Alright. Number four, use projects in custom GPTs more than you think. My gosh. This is good for me to say out loud because I'm also reminding myself. I literally thought, like, two days ago. I'm like, okay, Jordan. This is, like, the third time this hour that you just clicked new chat when you should have either went and found the project you already built, or you probably should have built the GBT or something like that.
Jordan Wilson [00:14:16]:
Alright. So we cover this in our free courses in our, everyday AI inner circle community, but there's a lot of crossover, between GPTs and projects, and there's also some unique features. But, the simplest way to think of them is, projects in GPTs are ways that you can, without any code, create customized versions of ChatGPT and attach your data to it. Alright? But it is sometimes easier or human nature or the default of using a nice interface like ChatGPT to just always go and find that new chat button and get to work. Right? Take the time. You know, projects are a good way to organize your chats, from a hierarchy. Right? Like a folder file type hierarchy. Right? But there's other benefits to there putting in custom instructions, uploading your, files in there, as well as the the other, you know, big version that I love or the big, advantage that I love with projects, having project only memory.
Jordan Wilson [00:15:21]:
That's huge. Right? Especially when you can share that with your team. Alright. Number five. This one, little confusing. Stick with me here. Connectors. Not connectors anymore.
Jordan Wilson [00:15:36]:
Alright. Yeah. This is another one. It kinda snuck under the radar, around the, holiday season here in The US. So this this is only a couple of weeks old. But connectors are kind of phased out. But if you had a connector connected previously, it still works. If you didn't know, a connector is just, an an easy way, to connect your version of ChatGPT to business data.
Jordan Wilson [00:16:02]:
So there's, there were connectors, for things like, you know, Dropbox, Box, you know, Microsoft SharePoint, different email service providers, Gmail, Google Drive. Right? There's dozens, actually, different, CRMs, different project management, and it just brings all of your data in. Right? So it's mini rag. We talked about that on the show yesterday. So connectors are now technically called apps, and some of the features and functionality has changed as has the terminology. So just FYI, and we went over this, I don't know, probably five or six months ago when connectors first came out. But there's three different types of connectors. There were chat connectors that were kind of instant, and they indexed everything.
Jordan Wilson [00:16:49]:
There's deep research connectors. So certain connectors that are available via deep research, which is really cool to do. Right? To only run a deep research on your company's data. Mind blowing. And then there were synced connectors. So that's something that you didn't have to, like, wait for. Like, the model didn't have to go and agentically search around your, you know, Google Drive or something like that. They're synced connectors that essentially were just always indexed.
Jordan Wilson [00:17:14]:
I believe, like, some of those were, like, calendar, your Gmail, etcetera. So now, again, some of the functionality is a little different, but the terminology is definitely different. So the chat connectors are now called apps with file search. The deep research connectors are called apps with deep research, and the synced connectors are apps with sync. But like I said, a little bit of the feature features and functionality, not quite the same. I'm still getting used to it. Like I said, I have a lot of different accounts. You know, there's other there were other connectors that were in a business plan, that weren't previously in a, that weren't in, like, even if you were on the pro plan, the $200 a month plan.
Jordan Wilson [00:17:59]:
So there were certain connectors that were only available if you had a business plan. So I'm still going through and, you know, testing out all the apps and, you know, trying to remember or looking at old videos to see, like, okay. This is how the connectors work. Do they work the same? So OpenAI is making a big push, with Chad GPT apps. I think it's great in the long run. Right? But it's I think there is gonna be a little bit of learning curve for everyone in the short run, you know, figuring out how these apps work. But regardless, you know, being able to connect chat g b t with very little, technical know how. You don't have to know anything about retrieval long minute, retrieve retrieve a long minute generation.
Jordan Wilson [00:18:40]:
You don't have to know about vector databases. You can just click a couple buttons, and the world's most powerful large language model has access to dynamic data from your company and you don't have to do really anything, that's amazing, and it's something you shouldn't be skipping over. Alright. Number six, ChattGPT is best for teams, Period. The future of work, I've been saying this good thing I have receipts on on the website. I've been saying this for a long time before it was a popular thing to say the AI operating system. I've been saying that for a very long time. I do think in the same way that, you know, through the nineties and the early two thousands, right, most businesses made a choice.
Jordan Wilson [00:19:20]:
Right? Are we a Windows, organization? Are we a Mac organization? Are we a Linux or, organization? You have to do the same thing with an AI operating system, and you have to move your day to day business processes your entire team. You better just do it now. There's no need to wait, but you should be using moving your day to day processes inside either a Teams or enterprise or sorry. A business or enterprise account in Chatt GBT. Google Gemini has the same offerings. Right? They have a business and an enterprise account that's different from their normal Gemini. Same thing or similar thing with, Claude. You know, Copilot's a little bit of a different story since it's more desktop, based.
Jordan Wilson [00:19:58]:
Right? Even Grok, you know, just came out with the business version, although I wouldn't touch that, with a 30 foot pole. That's just me. But, I mean, look at some of these stats. So 92%, and this is all from OpenAI's, enterprise report that just came out a couple of months ago. So 92% of Fortune 500 companies use ChatGPT or OpenAI technology. There's over a million business customers with 7,000,000 active ChatGPT work seats. Weekly messages in chat GPT enterprise increased eight x over the past year. Usage for enterprise, eight x increase.
Jordan Wilson [00:20:33]:
Crazy. Speaking of those projects and custom GPTs, 19 x increase, year over year for those more structured workflows according to OpenAI. Here's a big one. 320 x. A 320 x increase in reasoning token consumption. So, yeah, if you think, companies, yeah, they're they're just, you know, maybe giving some people a couple seats and, you know, toying no. People are putting their highest resource workflows in rebuilding the future of how their company works inside of Chatt GPT. Also, a, their study said that heavy AI users save more than ten hours per week.
Jordan Wilson [00:21:20]:
Okay? So, yeah, the future of Chatt GPT is collaborative work inside of a business or an enterprise accounts. Alright? And there's so many of those great features. Like I said, being able to share projects and shared project memory, that's huge. Right? Yeah. People think of projects as like an organizational folder. I like to think of it as an insights and answer machine. And then when someone else goes through it and they get an answer or an insight, if you have that project memory enabled, now everyone on the team that's in that, project has access to that because it has memory of a different user chatting in that project. That's a that's that's an enormous unlock.
Jordan Wilson [00:22:05]:
Right? The same thing with GPTs, right, that you can no code, have a GPT that does amazing things. It can, you you know, read and write code. It can go through, and you can build, you know, certain functionality that would normally, you know, three years ago, take millions of dollars. Anyone can do it. No code, low code, and then share it across your organization. Right? This is so much untapped potential there. Alright? And then, rule number seven, leverage chain of thought summaries as your secret weapon. I I talk about this randomly, on the show, but if you really wanna separate yourself from being, or your company or your department, you need to be doing this.
Jordan Wilson [00:22:47]:
Right? I cannot emphasize them enough, especially if you're using a model like, GBT five two pro. Look at the chain of thought. Right? I was actually, you know, talking about this, a couple months ago with my, with my stepdad. First time showing him a thinking model, and, you know, he has a background, in in chiropractic consulting. And I showed it to him, and he was like, wait. He's like, this is exactly what I would have done. He was kinda, like, blown away. You know you know, him and my mom, they they use the normal version of of chat GPT.
Jordan Wilson [00:23:23]:
Right? But when I showed him this, and I'm like, okay. We're gonna click this button, and we're gonna see step by step exactly, what this model thought. And then, you know, we need to verify. We need to make sure, is it doing the right thing? And he, you know, took, I don't know, five or ten minutes, you know, read through all the steps, all the sources, you know, gave it a pretty complex problem. I built him a GPT, and he's like, wait. This is exactly the steps I would have done, to solve this, you know, kind of, difficult case. So, you need to be leveraging the chain of thought summaries, as your secret weapon because sometimes they're not gonna go right. And normally, that's because maybe you didn't give it enough context.
Jordan Wilson [00:24:01]:
Right? The whole context engineering thing, yeah, that's extremely important. Alright. So, that's a wrap for the seven rules. Now we're gonna quickly jump in. We're gonna do a little bit of learning live. What could possibly go wrong? Right? Well, this is live ish. Alright. So, because some of these things take a long time, and I didn't want this to turn into right.
Jordan Wilson [00:24:21]:
One of my goals for 2026 is to hopefully always keep shows at, like, thirty three ish minutes or less, and sometimes doing the live live demos. Right? I'll just be sitting there chatting, waiting for something to finish for, like, fifteen minutes. We're not gonna do that to you, but we are gonna at least look live. And I'm gonna show you, I'm gonna show you all, hopefully, a pretty good, example or two exactly some of these seven rules. Alright? And I'm doing a very, very simple, use case here, and I think we're gonna be, I I I think we'll tackle at least, at least six or seven of these kind of different rules here. Alright. So, and, again, as a reminder, if you are only listening on the podcast, this isn't a super visual walk through, but it might be helpful. Okay? So, make sure for the video version, go to youreverydayai.com, or you can check the show notes in the podcast.
Jordan Wilson [00:25:15]:
Just click on it a little bit easier. Click on the episode page. Alright. So let's start. So I already did this right. I put the cake in beforehand. The cake's done, but I'm gonna show you all the ingredients, show you how we put in the oven, all that good stuff. So what I started with here is I started by using connectors.
Jordan Wilson [00:25:34]:
Alright? Or in this case, I think I'm on let me zoom out here. Yeah. I'm on my, one of my business plans. So in the business plan, there's something called company knowledge. Okay? So those are previously those were connectors, but in a team plan, they're still kind of formatted a little bit differently. But okay. So this would be using a connector or an app. So what I did is I said, in my Google Drive, find the Google Doc titled twenty twenty six AI predictions.
Jordan Wilson [00:26:03]:
Please give me a high level overview of what's inside this doc. So, you have a little drop down over in the, near the input area where you would put a prompt in chat GPT. Alright. So if you are on a, a team, you know, or sorry, a business or an enterprise plan, you can toggle all your different data sources on or off. So what I did here is I, you know, toggle everything off except Google Drive. Right? And then it went through and it found that. And I was using a thinking mode. I used, g b for this one, I just used g b t five two, thinking.
Jordan Wilson [00:26:40]:
Oh, yeah. Because on my on my team account, I think I was out of pro, queries. Alright. So here's what it did. It went through and, well, it pulled from that document, and I can see because if I hover over, I can it's kind of like cited or sourced there, and I can see it went through that. Alright. And it's it's actually funny how I came to this document. Right? I didn't manually make this.
Jordan Wilson [00:26:59]:
I don't manually make stuff anymore. But I was actually just on Twitter with chat g b t's Atlas browser. And I just had it scrolled Twitter for, like, an hour and just be like, hey. Anyone that's sharing, you know, twenty twenty six, you know, AI predictions, go through, you know, write them all down, put them in a document. So that's actually what this is. So it went through, you know, everyone from Sadia and Nadella to, you know, Logan Kilpatrick, who's been on the show a couple of times, Sam Altman, Ethan, Ethan Mollick. So, you you know, some well known people who are sharing kind of their thoughts on 2026 and AI. It went through, grabbed all this, then this, simple g p t five two query, but using, connectors went through and it pulled all this off.
Jordan Wilson [00:27:44]:
It's correct. Nothing's hallucinated. I went through and looked at it. Everything's accurate and cited. Cool. So now here's where the context switching comes in. So what I did next is I switched over to deep research mode. Alright.
Jordan Wilson [00:27:59]:
And if you haven't used deep research mode, you'll kind of, love it. It's, again, takes seven to eighteen minutes, give or take. Right? But in this case, I allow deep research to go to, my Google Doc in the web. So here's what I said. I said, great. So after it went through, summarize that document. So I said, please research these main core recurring themes from the Google Doc title twenty twenty six AI predictions. Shoot for between 12 to 15 common themes you spotted from these predictions.
Jordan Wilson [00:28:30]:
And then I said, find trends, supporting facts, gaps, connected dots, reasons the predictions may or may not come true. Also, give me a category for the prediction. Right? So I'm having it do some, you you know, turning, unstructured data into structured data. So I'm having it give me a category for the prediction, a likelihood score out of 100 based on your research, and a list of industry or sectors that these predictions may impact the most. Please make sure your responses are formatted in a cohesive and consistent way across the 12 to 15 common themes you identified. Alright. So there we go. It first asked me some clarifying questions, which is something I love.
Jordan Wilson [00:29:10]:
I love that Chat GPT's deep research has done this since day one. I wish all the other deep researches would do this because before it goes off on a fifteen minute adventure, hopefully, down the right route, you wanna make sure it has the right information. Alright. So I went through. I answered the questions. Then it went in. It got to work. So, let me just see.
Jordan Wilson [00:29:32]:
Is this the right one? It is, the wrong tap. Okay. So, same same thing, just different tap. Alright. So after after that, it went through and it did the deep research. Alright. So let's skip over. Let me zoom out a little bit here on my screen, and let me skip over to, number seven.
Jordan Wilson [00:29:55]:
Right? How you can leverage the chain of thought summaries as your secret weapon. So this a lot of people don't know this because it's kind of hard to see. It's usually in grayed out font, anytime you use a thinking model, or anytime you do deep research. In this case, it says, you know, research completed in thirteen minutes, 28 sources, a 160 searches. So I can click that, and then it's gonna pop out on the right hand side. Essentially, this is a summarized version of chain of thought. So this is the difference between, you know, the old school transformer or non thinking models that are essentially just next token prediction. Right? Non thinking models that are essentially just next token prediction.
Jordan Wilson [00:30:32]:
Right? You know, there's some other things, top p, top k, right? Not getting into that. But the thinking models think like a human. So I can go through and read it on the right hand side. So before it got started, it did, you know, went into my Google Drive. It thought about some things, went back into the Google Drive. Right. So it I I can literally see how the model is tackling this problem. So this is the same thing if I had a team of researchers, but they couldn't think in their head.
Jordan Wilson [00:30:59]:
They had to just talk out loud. This is great. Right? And this is the way I think you ultimately go from an average chat GBT user or, an average, you know, AI native team. Right? All those buzzwords to actually being able to crush the competition. The issue is, right, these models always change under the hood. Things are, you know, usually, hopefully, getting better, but definitely changing. So you have to be able to read, how the model tackles this problem, what they do in the right order, all these different things. Alright.
Jordan Wilson [00:31:31]:
So, what we got from the output was a very impressive, report. Here, it says the analysis of core themes in twenty twenty six AI predictions. There we go. It went through, found some supporting trends and challenges for these different, core themes that it identified from the original list off Twitter that Atlas went and found on its own. Pretty cool. Right? Alright. So I'm scrolling through these. You know, if you want, I'll probably share this in the newsletter, today.
Jordan Wilson [00:32:01]:
So, or no. You know what? If you repost this on LinkedIn, I'll bump you to the top of the, the, everyday AI inner circle list, and I'll send you all this stuff if you want just because it's pretty fascinating. It's good stuff. Right? Okay. So going down going down to the bottom. A long report here. Wow. Okay.
Jordan Wilson [00:32:20]:
So here we go. Now we're going into, back to, I think, number four, which was using projects in GPTs more than you think, but also, a little more context stacking here. So, what I did is hit the app key. Alright? And then I have a GBT that I built called the SaaS dashboard canvas. So all this is, it's a GBT that I use frequently. I continually update. More or less, I I I tweak it, so I don't have to type out a long prompt each time and iterate. All this GPT does is it takes whatever information is in that context window, and it essentially builds the equivalent of a SaaS dashboard or a KPI business dashboard without you having to do anything.
Jordan Wilson [00:33:13]:
So it uses canvas mode. Right? We're not going into all the different modes, but, you know, I essentially have that enabled on the back end of the GPT. Again, you don't have to know code. You don't have to know anything. It's simple. There's actually a literal GPT builder. You can just talk to it and say, here's what I want this custom version of GPT to do. And then the cool thing, like you just saw here, and I did that part live.
Jordan Wilson [00:33:36]:
Right? Anywhere in a normal chat, I can just click that at button and start typing just like if you're tagging someone on Microsoft Teams or Slack or anything like that or social media. Right? I can go find that GPT that I've given, specific, a specific role to. Right? I can put files in there, etcetera. But all this one does is it uses the entire context window. It uses canvas mode, which can write and render code, and it's gonna build me a SaaS dashboard, and I don't do anything. Alright. So then I scroll down here. I see, again, looking, I can pop out the chain of thought, you know, saying, oh, I'm supposed to use canvas mode and, you know, build something, and I can click the preview here.
Jordan Wilson [00:34:15]:
Ready y'all? Let's see. Is it gonna work? Bam. It works. Okay. Podcast audience. This is really freaking cool. Alright. It built a really slick dashboard.
Jordan Wilson [00:34:30]:
Let's see if it's interactive. Let's see if it works. I haven't tried it yet. Okay. That's that's pulling the slider. Okay. So there's a, there's a search. Oh, this is really, really cool.
Jordan Wilson [00:34:43]:
So there's a thing that says an average likelihood. Right? Because I had it, quantify a lot of these these stuff that I didn't wanna spend the time to quantify and categorize. Right? And what's crazy is, technically, Atlas ChatChippity's browser went out and found all this stuff anyways, put it in a document, then I had ChatChippity go through. This this is this is how I like to learn. Right? I don't like reading plain text anymore. I like agents going out and doing things for me, bringing back. You know, I use my taste and my feedback, you know, my orchestration skills, whatever you wanna say, but I love interacting with data. So I have an an extremely, nice looking dashboard based on all of this.
Jordan Wilson [00:35:22]:
Right? So it says the average likelihood, the top impacted sectors, the highest likelihood. There's a search bar here. There's a drop down category that works very cool. There's a slider. So if I just wanna see the ones that are, you know, that have a lower likelihood, or if I just wanna see the ones that have a higher likelihood of coming true based on the deep research, based on the, the GBD five two thinking, based on the, you know, on the Atlas scrape. Right? Very cool. So I can scroll through here. Cool looking dash dashboard.
Jordan Wilson [00:35:55]:
There's a strategy lens here, with different tabs I can click on. This is an extremely impressive dashboard that it put together. So there you go. That's it. That's a wrap. I gave it to you. This isn't all of it. Right? But if you could stick to these seven things, these seven rules, your personal growth, your company's trajectory, your businesses outcomes are going to drastically improve.
Jordan Wilson [00:36:29]:
Period. Let me tell you again the seven rules, and we're gonna wrap up. Ready? Number one, do not use the free version of chat g p t. Don't do it. Number two, you should almost always use thinking models. Number three, context switch between the right model, mode, or GPT. Four, use projects and custom GPTs more than you think you might. Number five, still use connectors even though they're apps.
Jordan Wilson [00:36:52]:
Use apps or connectors, whatever they're called technically. Number six, use chat g v t for teams. It is a cheat code. And then number seven, last but not least, leverage chain of thought summaries as your secret weapon. Read them, iterate, reiterate, improve things. Dang y'all. I hope this was helpful. Now you know how to use chat g v t a little better than before, but if you really, really wanna become an actual pro, if you think this was good, the good stuff is inside of our free community.
Jordan Wilson [00:37:21]:
That's right. Alright. And if you want earlier access than everyone else, and I'm sorry, so many people emailed me. I've I've some I forgot to email back. Some ended up in my spam. I did a terrible job. There's a lot of people waiting. I'm gonna be working hard, working overtime, getting people into the free community, getting people access to this course that is freshly updated, and we're gonna continue to update it.
Jordan Wilson [00:37:42]:
Probably, anytime Chadwick D comes out with an update, we're gonna update the course. It's gonna stay. You take it at your own pace. It's pretty good. Right? But if you want access, just repost this show, on LinkedIn. I'll bump you to the top, get you in there as quickly as possible. And, again, FYI, what do I what do I repost this LinkedIn post? So if you're listening on the podcast, go look in the show notes. There's always something that says, you know, join the conversation on LinkedIn, find today's show.
Jordan Wilson [00:38:08]:
Go click that, repost this. Alright? And let's all dominate 2026 together by being a little bit better at Chat GPT than we were yesterday because it's more than just a personal tool. It's more than just an AI chatbot. I do strongly believe, right, not just Chat GPT, Gemini, Claude, Copilot, everything else, but Chat GPT. Right? It is the future of how we all work. So let's all dominate 2026 together. I hope this one was helpful. Thank you, y'all.
Jordan Wilson [00:38:39]:
If you haven't already, please go to youreverydayai.com. Sign up for the free daily newsletter. We'll see you back tomorrow and every day for more everyday AI. Thanks, y'all.