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The Power of Google 2.5 Pro Unlocked
In the realm of artificial intelligence, Google's Gemini 2.5 Pro is setting the benchmark with its groundbreaking capabilities. While the technical prowess of this model is compelling, its implications for business use are even more profound. Here we explore the specific ways in which businesses can leverage Gemini 2.5 Pro to enhance productivity, efficiency, and innovation.
Advanced Content Analysis and Creation
Gemini 2.5 Pro goes far beyond plain text generation — it’s built to read, transcribe, and extract structured data from complex, image-heavy documents such as pitch decks, scanned PDFs, and slide decks. The model can identify text, tables, charts, and diagrams, preserve layout when asked, and output structured formats (CSV/JSON or editable Google Sheets) so teams can immediately analyze numbers or reuse content in workflows. This makes extracting KPIs from a Canva pitch deck or converting a chart into a spreadsheet far faster and more reliable than manual copy-and-paste.
Interactive Canvas for business apps and training
Gemini’s Canvas functionality lets you turn research, slides, or static content into interactive experiences — think onboarding quizzes, memory games, visual dashboards, or micro-apps — without building a product from scratch. Canvas pairs Gemini 2.5 Pro’s multimodal understanding with rapid prototyping tools so you can convert training materials into engaging, measurable learning flows that increase retention and reduce time-to-competence for new hires
Real-time research, trend spotting, and sentiment signals
When paired with Deep Research and web exploration, Gemini 2.5 Pro can surface up-to-date signals from across the web — summarizing news, spotting emerging trends, and helping quantify sentiment for brands, products, or topics. That means you can rapidly generate a concise brief (for example: “public sentiment for Brand X over the last 30 days”) and get both the qualitative takeaways and the data points you need to inform PR or product responses.
Huge context window and developer-grade coding
Gemini 2.5 Pro supports very large context windows on paid tiers, enabling the model to keep track of extremely long documents, full codebases, or multi-file research projects without losing earlier context. This is a practical advantage when you’re working with long legal contracts, extensive technical specs, or iterating large software projects — the model can help draft, refactor, and generate runnable code or prototypes that accelerate engineering and reduce dependence on scarce specialist time. (Access levels and token limits depend on plan; Pro tiers unlock the largest context windows.)
Seamless handoff to spreadsheets, dashboards, and automation
Gemini integrates into common productivity flows — for example, you can export parsed table data and charts directly into Google Sheets and then apply formulas, pivots, or charts for immediate analysis. This reduces friction between insight generation and action: convert a document into an editable dataset, run analyses, build dashboards, and automate reporting without building glue code.
Together, these capabilities translate into faster decisions and lower operational overhead. Teams can turn slide decks into data, convert training into interactive learning, run near real-time market scans, and iterate on software ideas with AI-driven prototypes — all without losing fidelity or context. For product, marketing, or operations teams, Gemini 2.5 Pro moves AI from a “creative assistant” into an integrated, multimodal partner for analytical, interactive, and production workflows.
Topics Covered in This Episode:
- Innovative Uses of Gemini 2.5 Pro
- Analyzing Gemini's Built-in Thinking
- Real-World Business Applications
- Live Demos of Gemini Features
- Gemini's PDF and Image Analysis
- Interactive Content with Gemini Canvas
- Gemini's Onboarding and Training Tools
- Gemini's Unmatched Processing Power
Episode Keywords:
Gemini 2.5, Google Gemini 2.5 pro, Google's new Gemini, Google's Gemini update, Gemini 2.5 pro unlocked, Large language model, Multimodal, AI Studio, Chain of Thought model, Chain of Thought reasoning, 1,000,000 token context window, Transformer model, Advanced coding, AI Studio vs. Gemini, AI Studio can’t turn off data training, Human preference, Benchmark scores, Self-help coding, AI-powered coding, Business use case, Coding capabilities, Hybrid model, Internet-connected models, PDF transcription, Sentiment analysis, Visual memory game, Interactive quiz, Chicago vibes HTML, Business dashboard, Interactive SOP, Gemini in education, Privacy in AI, Free access to Gemini 2.5, Built-in thinking, AI chain of thought.
Podcast Transcript
Jordan Wilson [00:00:16]:
Alright. So if you haven't heard Google's new large language model update in Gemini 2.5 pro is good. It's like really good as in benchmarks, the best human preference, the best. But what can it actually do for your business? I think this is something that we're always thinking about. I think early on, you know, in the chat GPT days, we we got into this kind of rut, right, when large language models first came out and we thought, okay. Well, they're just for creating content. Right? This is to help me write a blog post, or a large language model is to help me, you know, write something for LinkedIn, or improve an email, to a colleague. Yes.
Jordan Wilson [00:01:04]:
Large language models are good for these things. But what about when we talk about state of the art multi modal, large language models like Gemini's, sorry, like Google's new Gemini 2.5 pro. So today I thought we'd have a little bit of fun and maybe a little bit of chaos as we go over, in part two, Gemini 2.5 pro unlocked exploring everyday use cases. Alright. I'm excited for this one. I hope you are too. What's going on y'all? My name is Jordan Wilson. If you're new here, thank you for joining us.
Jordan Wilson [00:01:40]:
This is Everyday AI. This is your daily livestream podcast and free daily newsletter helping us all not just keep up with AI, but how we can all actually use it to get ahead to grow our companies and to grow our careers. Is that personal? Is that you? Is that what you're trying to do? If so, this is step one, listening to this podcast or livestream. Step two is going to our website at youreverydayai.com. Here's what we do. Two main things in our free daily newsletter. One is we recap and sometimes summarize, you you know, the, the episode for today. You know, sometimes I have guests on today.
Jordan Wilson [00:02:14]:
It's just me talking about Gemini two point five. So we give you what you really need to know and pull the valuable insights, from each day's episode as well as keeping you up to date with everything else happening in the world of AI. So make sure you go to our website, youreverydayai.com. Sign up for the free daily newsletter there. Alright. So, normally, we we go over the AI news and all that in the beginning of the the live stream. This one could be a longer one and I'm trying not to. So, if you do want the AI news, we're gonna have that in the newsletter.
Jordan Wilson [00:02:43]:
Alright. So I'm excited, and I hope I can get a little bit of help from our livestream audience today. So, thank you for tuning in. Dennis joining us from, New York City. Yeah. Where are y'all from? I should ask this more. Right? I like to I like to know where our livestream audience is from. Brian joining us from, Minnesota.
Jordan Wilson [00:03:01]:
Kyle, thanks for tuning in. Michelle, big bogey, Sandra, Jay, everyone else. Thank you. But I might I might be asking some some help from you all today. Alright. But, let's just get caught up. Right? So I did an entire episode yesterday, on what's new in Google Gemini, 2.5. So, if you do want to know, just scroll back one episode.
Jordan Wilson [00:03:24]:
Maybe if you're listening to this on the podcast, it's episode four ninety four where we just went over the basics of, Google, Gemini 2.5. But as the world's fastest, recap here, here's here's kind of the the the super simplified version of, what's new, okay, in Gemini 2.5. So it has built in thinking. That's the biggest one. It is technically a hybrid model. It can, it, combines the kind of old school, quote unquote, transformer model with a reasoning or chain of thought model. So you'll see that as we do some live demos here. And it's gotten very impressive scores, not just on traditional benchmarks, but also, some newer benchmarks like, humans, humans last exam, humanity's last exam where it scored way better than any other large language model.
Jordan Wilson [00:04:14]:
It does have a enormous 1,000,000 token context window. So that is more than 1,500 pages. As an example, 30,000 lines of code before Google Gemini 2.5 pro begins to forget things. I will let you know and will probably see here live. That is when you are using it in AI Studio versus the front end of Google Gemini. More on that in a minute. Probably one of the biggest leaps in terms of capabilities and maybe this will be applicable for your business, maybe not, is the advanced coding. So Gemini 2.5 is very, very good at coding.
Jordan Wilson [00:04:51]:
Alright? And you might be thinking, alright, Jordan, that's not me. I'm not a software engineer. Right? Okay. If you listen to our 2025 AI roadmap and prediction series, I said in 2025, everyday nontechnical people are gonna be using large language models to spin up their own apps, to spin up, their own, you know, I don't know, Chrome extensions, their own, desktop apps that help them do things better. We're not there yet, but I think we will be there very soon. So keep that in mind. You know, just because you're not a current coder or developer or software engineer, you should still, I think really pay attention to this, Google Gemini 2.5, the big leap, in coding and maybe some of our use case examples will show that. You you know, number one benchmark ranking.
Jordan Wilson [00:05:35]:
That's huge. So the biggest thing we talk about, there's all these benchmarks that I think sometimes, you know, AI labs, overfit for. But, when it comes to Elo scores, inside the chat the LM chatbot arena, that's human preference. Right? So people put in all kinds of prompts, you know, write a blog post, create, you you know, write code for this, you you know, generate a creative outline for, you know, or strategy for x. Right? And you get two responses. You don't know who they are. You choose which one is the winner. And Gemini 2.5 pro has literally broken the record for the biggest leap into the number one spot.
Jordan Wilson [00:06:14]:
Normally, when a new model comes out, you know, from OpenAI, from Claude, etcetera, it'll usually get the number one spot. Right? Because generally, it is, you know, between two to six months, between big models, especially earlier in 2024. So, you know, usually, the the top model would come in a couple points higher, couple preference points higher. Gemini came in at 39 points higher, than, what was in second place. Now what's in second place right now is g p t four o. The other, big recap here, it's free. Was not expecting this. So Google did not even announce this in their initial two, Gemini two point five announcement.
Jordan Wilson [00:06:49]:
They quietly put it out in a tweet over the weekend. Right? But even if you don't have a paid account of Google Gemini, you do have access to Gemini 2.5 pro for free. The limits are, a little more restrictive. Alright. So, one more thing one or two more things before we get started here. So, in in in livestream audience, you know, if you have any things you wanna try live, let me know. Maybe, I don't know, in your comment, I should have thought about this, but, like, beforehand. I don't know.
Jordan Wilson [00:07:19]:
Maybe put two stars. Alright. And then I'll see if I can maybe copy and paste it. I don't know if I'm able to, but I'll try. Or at least I can try to, you you you know, get the gist of of what you wanna see. But before we get started, a couple things to keep in mind. Our podcast audience, thank you for tuning in. Y'all are awesome.
Jordan Wilson [00:07:35]:
I never would have thought when I started this thing, this would be a top 10 tech podcast, but this is one of those you might wanna check out the newsletter so you can come and watch the video. You can always rewatch it on our website, on YouTube, on LinkedIn. I'm gonna try my best, to verbally describe what's going on with this is unfortunately gonna be a very verbal or or or sorry, a very visual episode. And this is something, this is always the number one request we get. Right? Do more live demos. Do more live demos. So, you know, podcast audience, I'm gonna try my best, but this is one you might wanna come watch the video on. Another thing to keep in mind, AI studio versus Gemini.
Jordan Wilson [00:08:12]:
Okay? Gemini is the front end chatbot for Google. AI Studio is kind of a sandbox for developers, although it's not as hard as you may think. Right? You know, there's some initial setup, but then after that, it's pretty easy. You know, if you're on a paid plan of the front end Gemini chatbot, you can, turn off model training which is important. Right? Because you should never be sharing, you know, proprietary, sensitive, PHI. Right? Like, private health information into a a a chatbot. But if you are using AI studio, there's no turning off data training. So AI studio is free.
Jordan Wilson [00:08:51]:
That is actually where you get the more powerful version of Gemini 2.5 because you get the, the entire, context window in some other, controls, that you don't get on the front end of the Gemini chatbot, and hopefully, I'll be able to demo that here in a minute. But just keep in mind, Google's AI studio is free, but you cannot turn off data training. If you are on a paid plan of Google Gemini on the front end on the chatbot, you can turn off training. Alright. The other thing, I'm doing this live y'all. Alright. So bear with me. But I think it's actually important.
Jordan Wilson [00:09:22]:
Right? Because if you go watch anything online, you know, there's some great creators out there, you know, who put together, you know, demo videos and all that. I know a lot of these people. I talk to them and I know how long these videos take. Right? So, sometimes to to to put together a couple demo use cases of something like Gemini 2.5, it might take them five hours of recording, for a twenty minute video. Okay? And a lot of editing to make sure it looks right. I don't like that. You know? People are always roasting, roasting me on our YouTube channel because it's like, oh, your production quality stinks. And you have all these mistakes.
Jordan Wilson [00:10:00]:
And sometimes you stutter or say the wrong word. I'm a human. Right? This is live. This is unscripted. This is unedited. This is just you know, but I think it's important because I think so much of these of of these demos of all large language models that you see, all AI tools are overly polished. They're manufactured. You know, in some cases, you you know, they're being artificially pumped and promoted on the back end to make you think there's something that they're not.
Jordan Wilson [00:10:24]:
This is real. This is live. This is unedited. Alright? So keep that in mind. Live demos with Generated AI are a terrible idea. Right? But you all like them. You all wanna see them, so we're gonna do them. And so far, my takes right now with Google Gemini, it has an extremely high ceiling, but a finicky floor.
Jordan Wilson [00:10:44]:
Alright. Let me let me kinda describe, what I mean by that. So here's here's an example, and, you you know, I put this out on Twitter, and I'm gonna ask the Google team about this. But, it's it's keep in mind, Gemini 2.5 pro is experimental. Alright? Very experimental because sometimes you're gonna get a weird, a a weird result like this. Right? I always have a series of prompts, that I use to, especially for Internet connected models, so I can make sure that they're correctly pulling information. Right? When we talk about the role of human in the loop, it's very important. And as large language models get more powerful, more robust, more features like Gemini two point five, I think us humans think, oh, we can sit back and relax.
Jordan Wilson [00:11:24]:
We actually have to be more vigilant. The more that we hand off to large language models, the more that we have to I like to think of it as expertise in the loop. Right? Not human in the loop. Human in the loop just thinks like, okay. You know, I'm gonna blindly, you know, do my human job here. This looks good click. No. You have to apply your expertise.
Jordan Wilson [00:11:40]:
This is an a simple example. Right? But, I said, what's the latest episode of the Everyday AI Show by Jordan Wilson. Right? I wanna see if Google Gemini 2.5 can get my episode from yesterday. Right? And in this example, you you know, because it is a hybrid model, I could even see the thinking, and it says, the user is asking for weather forecast in Chicago, Illinois for today, 04/01/2025. I should use a weather tool to get the current weather and forecast for Chicago. Number one, not true. Right? It didn't. Number two, not surprisingly.
Jordan Wilson [00:12:11]:
Right? It picked up my location without me telling it. Alright. So keep that in mind. It's finicky. It's experimental, but when it works, I am very impressed. I am very impressed. Alright. Let's get wild.
Jordan Wilson [00:12:25]:
Let's get wild y'all. Please live stream audience. Can someone tell me if you can see, see the screen here? I'm gonna be jumping between some, some tabs here. But if you could let me know because I don't wanna do another twenty five minutes of the show and bringing you guys these live demos, and you're like, oh, Jordan, you weren't sharing your screen at all. Kimberly says, we need to see more bloopers too. It's a part of life. Yeah. I think that's how you learn generative AI.
Jordan Wilson [00:12:52]:
That's how you get better at large language models. You you try them. Right? No one's an expert. Right? Or I I I I won't say no one. There's very few people that have been working in large language models since, you know, for ten years. There's a couple people. Right? But most of us, you know, you have to learn on the fly and you learn by failing and you learn by making it better. Alright? Dennis, thanks, Dennis.
Jordan Wilson [00:13:14]:
Dennis said AI is cool, but we love humans more, Jordan. Okay. Cool. Alright. Thank you, Nicole and Kimberly, for letting me know and char, Charles that you can see the screen. Cool. Let's let's let's get after it. Alright.
Jordan Wilson [00:13:26]:
I'm gonna be jumping around a little bit here y'all, and, I apologize if you hear, like, a lot of clicking. Alright? That's my mouse. I should probably figure out how to, you know, not pick that up in the podcast. Alright. So I'm gonna go in and, upload a file. So first, I am right now, I'm on the front end of Google Gemini, in in your drop down, you have 2.5. One thing to keep in mind, and maybe this is a hack for chat g p t, there's no model switching, which I wish there was in Google Gemini on the front end. So as an example, if I start in two point o flash, you know, I'm just gonna say sup.
Jordan Wilson [00:14:02]:
Alright. Now if I want to model switch or start working in, 2.5, I can't. It refreshes that chat. So why why does that matter? Why is it important? Well, as an example, I'd love to, like, use deep research. So deep research inside Google Gemini has been upgraded to, Gemini two point o. It's actually amazingly good. But so if I wanted to, you know, do something in deep research and then go over to 2.5 pro, you can't do that. Whereas with Chat GPT, you can't.
Jordan Wilson [00:14:33]:
I think that's like such an underrated hack is just model switching, inside Chat GPT. But, you know, before we get started, it's worth pointing out. Alright. So, I am on the gemini.google.com. I have a paid account FYI, but even if you have a free account, you should be able to do this live stream audience. If you wanna follow them along, you know, you can do that as well. Alright. So I'm, selecting 2.5 pro experimental, from the drop down menu, and I'm going to add a file here.
Jordan Wilson [00:15:01]:
Alright. So I'm going to add a PDF here. Alright. So I'm going to describe what's going on as this happens. I'm gonna say, please. So I'm just saying I'm uploading a PDF, and I'm saying please transcribe every word of this. So this is about a, let me see how how many pages this is. It's probably about a 15 page PDF here.
Jordan Wilson [00:15:28]:
So these are, you know, people reach out and they're like, hey. I wanna, you know, advertise on the everyday AI podcast. So I have this, this little deck that I send potential advertisers sometimes. So, hey. If you do wanna reach one of the largest audiences, in artificial intelligence, you know, on our podcast, you know, make sure to reach out to me. But the thing is most large language models cannot read this, because I mean, number one, I made it in Canva. So, you know, most most large language models when they're using, computer vision, when they're using, sometimes OCR technology, they all work a little bit different. They really struggle with this because it's all essentially images.
Jordan Wilson [00:16:04]:
Right? It's not like, I I build this in Word and it's a bunch of text. This is very visual. Right? There's backgrounds. There's tons of images on each page. Right? It's it's a lot going on. So, you know, even to pull all of these words, I mean, we'll see. I've done some of these so far. Some I haven't.
Jordan Wilson [00:16:21]:
So, let's see how, Gemini two point five does. So I can click show thinking. Right? And I'm not gonna be able to do this for every single one, but it says I need to get the I need to get the relevant content to answer each user's questions. The user wants a transcription of the entire PDF document. I have I have the extracted text from the document, provided, by the content fetcher tool. So why I'm gonna spend a little bit more time on looking at the chain of thought. And, y'all, this is huge. Right? The thing I love about Google Gemini's chain of thought is you can see their tool usage.
Jordan Wilson [00:16:56]:
Alright? Which is going to help you get more out of the tool if you know because you can start to speak Google's language, and hopefully that will, you know, become a little more clear here when I try, another prompt here. So anyways, let me just go ahead and scroll down, and you'll see right away, it's breaking it down. Page one. Here we go. Everyday AI sponsorship opportunities, daily podcast, livestream, newsletter. Perfect. It's got the website. Great.
Jordan Wilson [00:17:22]:
Page two, it's got it all. Okay. This is really, really good. I haven't seen this, out of a large language model yet, and it's it's formatted. It fixes, you know, sometimes the the the the fonts look a little weird, you you know, but it crushed it. Alright. This is this is impressive, y'all. Alright.
Jordan Wilson [00:17:43]:
So, I'm going back. So at the bottom, I have trusted by leaders from. Right? Because we have all these people from big companies that have, you know, that read our email newsletter, that reach out to me, that that have given us testimonials, you know, from Google, Amazon, Nvidia, Microsoft, etcetera. Right? We have a lot of listeners. Yeah. If you wanna reach them. So not only did it get the text, but Google Gemini here, very impressive. Use computer vision and gave me just the names.
Jordan Wilson [00:18:11]:
Right? I didn't put the name, Google, the name Nvidia, the name IBM. Those were multiple images. Mind blowingly impressive. Alright. Page three. You know, partnership opportunities. So so good. So good.
Jordan Wilson [00:18:26]:
So I'm I'm I'm actually curious. And again, I'm doing a lot of this live. Did you guys know hey. Live stream audience. Did you guys know this? I I I didn't even know that it was gonna look at the images in this deck. You you know, I've tried this a lot with Chad GBT. I've tried it a lot with Claude. I haven't tried it with the updated version of four o that was just rolled out a couple of days ago, so maybe it'll do better.
Jordan Wilson [00:18:50]:
This is very impressive. Right? So I'm curious if it's even the pull, some of these stats. So I have, like, our ad channel overview, and there's, like, text within screenshots of this image. So, you you know, I'm I'm curious. I'm just gonna scroll down to, to that page. Let's see here. Add channel overview. Okay.
Jordan Wilson [00:19:08]:
It didn't pull it in, but that's fine. The text was probably too small, but it literally crushed it. My gosh. It even created I have a chart. This is so, so good. I have a chart, and it converted my little chart, which is just I made in Canva. Right? So not only, was it able to pull all that because a lot of it is images. It created a chart for me that I can export to sheets.
Jordan Wilson [00:19:32]:
So I can click that export to sheets, and then open in sheets. Bam. There it all is, our little breakdown. Just that right there is wild, y'all. How many times when we talk about business use cases, right, I don't know about you guys. I I read a lot of PDFs, right, or a lot of documents. Sometimes you may not have the version that you need. Right? It's like, oh my gosh.
Jordan Wilson [00:19:55]:
This was from Bill. He left two years ago. I have to redo this entire thing. Well, you can upload it into, Google Gemini 2.5 pro. It's going to transcribe the whole thing. If there's charts and graphs in there, it's gonna recreate them. You can open them, in Google Sheets. This one use case alone, wow.
Jordan Wilson [00:20:14]:
Wow. Very, very, very good. Alright. Hey. Cool. Sandra says she's doing it along on her computer. Alright. Let's do another one.
Jordan Wilson [00:20:23]:
And this is where I think we're gonna get some things, that go wrong, but, let's try it anyways. Alright. Because like I said, I I did try some of these. Some of them, I did not. So I'm saying find the 20 latest episodes of the Everyday AI podcast and give me a brief summary of each one, then find five trends between episodes. Alright? So think, what's your business use case? What are you following? And think, you know, obviously, Google Gemini 2.5 is connected to to Google. So one of the reasons I'm doing this, I think it's gonna fail. Alright.
Jordan Wilson [00:20:58]:
Here we go. Hey. We got a we got a live hallucination, y'all. Alright. So it says the user is asking for the date of Easter in 2025. Strangely enough, this is the exact same hallucination I got the first time I tried it. So I'm just gonna add one more, one more thing. I'm gonna put my name by Jordan Wilson.
Jordan Wilson [00:21:15]:
I don't think so. Last night, I did get this to work correctly, but I did get some interesting, some interesting insights by looking at the chain of thought, by looking at the different tools, that Google is using under the hood to pull this information. Alright. So now on the second time, it got it right. It didn't tell me the dates of Easter, which I don't know why it did it. Alright. So it's breaking this down. So it says, this requires multiple steps.
Jordan Wilson [00:21:38]:
One, oh, it just shrunk that. Okay. Did you guys see that live? It was working correctly. Everything was good. And then it says the user is asking for the top five rock songs released in 1977. Y'all, this is why I said I said this ahead of time. The ceiling is so high. The floor, so finicky, at least right now on the, front end of Gemini 2.5, Pro.
Jordan Wilson [00:22:07]:
So what we could do, I wasn't planning on doing this, but let's just do it anyways, y'all. Let's go into AI Studio. Alright. So, AI Studio, it is more of a developer tool or a sandbox, but it's actually very easy once you get it set up. Alright. So you can click the create prompt button, right here. You can choose the different models over on the right hand side. So a little different.
Jordan Wilson [00:22:30]:
I'm gonna try the same thing. Let's go to Gemini 2.5 pro experimental. I'm gonna turn the temperature down on this. Okay? The default is one for creativity. I want facts. Alright? And then I'm gonna go ahead and turn on so you can turn on and off different features. This isn't a full blown AI studio tutorial. I just wanna see if this will work, but I'm turning on grounding with Google search.
Jordan Wilson [00:22:54]:
So I have found when I get some weird, little hallucinations like you just saw on the front end of Google Gemini, when I usually, when I try it inside AI studio, it works a little better. Alright. So now I can expand, to see the chain of thought. So it's saying the user wants a list of the 20 latest episodes of the Everyday AI podcast, identify five trends. So it's looking up search queries. These are the search queries. What are the latest episodes of the Everyday AI podcast? Everyday AI podcast latest episodes list. Right? It developed a plan, and then it says, here are the 20 latest episodes of the Everyday AI podcast.
Jordan Wilson [00:23:31]:
Alright. I spoke too soon. I did not think Google, Gemini was gonna get this correct. We saw when we use the front end Google Gemini chatbot, it went off the rails. It's experimental y'all. It's it's it's it's gonna do that. Right? But inside Google AI Studio, very good job. So, interestingly enough, it got this a % right.
Jordan Wilson [00:23:53]:
So we got our latest episode, which was four ninety four from less than twenty four hours ago. So it did a good job. And then it got the most recent 20. Fantastic. And now I'm now it says five trends between episodes. So it says, there's been a focus on major AI players and models. Correct? Rise of AI agents and automation. Yep.
Jordan Wilson [00:24:13]:
Industry specific AI applications, impact on work and productivity, hardware and infrastructure importance. Great. So it did a good job of picking up, you know, some kind of some common trends, over the last 20 episodes. So, even though Google Gemini, the chatbot got a big fat failure, the, Google AI Studio, very impressive job. I've done, similar prompts like that between all the Internet connected large language models about six months ago, and none of them handled them, the way that Google's AI Studio, just did. Alright. Let's try another prompt here. Here's what we're doing.
Jordan Wilson [00:24:48]:
This one's a little tricky. Alright? I'm saying summarize this page and I'm giving it a boolean search, URL. Alright. I'll explain what that is. But the reason I wanna do this is to look at the tool use. Right? So look at the chain of thoughts. So you can click show thinking when you're using Google Gemini 2.5, and it says the user wants me to summarize the content of the Google search results page. And then it says the browse tool can be used to extract information from a specific web page URL.
Jordan Wilson [00:25:17]:
However, the URL provided is a Google search results page. The browse tool description explicitly states not to use it for Google search result URL. So instead, it's saying I can use the Google search tool. And this is a huge, I'm not gonna say cheat code, but this is gonna save you so much time once, you know, this Gemini 2.5 pro on the front end becomes a little bit more stable. Because now by looking at the chain of thought, you will know what exact tool that you need to call because Google doesn't necessarily tell you. Right? So just in case you're curious, this, this Boolean URL, it's essentially, like a Boolean search operator, that I use. I do this every day when I go and see what's the most important AI news. Right? But it's just search results for certain companies, you know, OpenAI, Apple, Nvidia, Microsoft, Amazon, Anthropic, etcetera, the latest news.
Jordan Wilson [00:26:09]:
So it's the least, the last twenty four hours, just AI news from those companies. So let's see, what Google Gemini ultimately did. So I just said essentially summarize it. Did a good job. Did a good job. So it says key trends. Major players are rapidly releasing enhanced AI models like Google Gemini 2.5, OpenAI's, GPT four five, anthropic claw three seven, IBM's granite three two. It did a really good job.
Jordan Wilson [00:26:40]:
Right? Even though I can't see exactly oh, did it go to all of these pages? Did it just look at the the headline and the meta description? It did a really good job. So think business use case. I love Boolean search terms or, you you know, Boolean operators. Right? Do that for a Google search for what you care about. Right? Maybe it's it's market research, maybe it's it's logistics. Right? Put in your competitor names, whatever. I think there's so much utility, for using just Boolean search and AI tools to quickly get you caught up, that on on things instantly that would normally take a very long time. Alright.
Jordan Wilson [00:27:16]:
Let's keep this train moving. Choo choo. Alright. Here's one I really wanted to do, but, we're not gonna have time. Alright. So I'll I'll I'll move on to another one here. Alright. Let's do this one.
Jordan Wilson [00:27:28]:
I'm saying, so for this one, I'm going to use Canvas. So this is another kind of update to the updates. So Google Gemini 2.5 pro was just released less than a week ago. And then over the weekend, Google did a lot of other updates to Gemini 2.5 pro. Number one, they said, alright. It's it's free for everyone. Number two, they rolled out Canvas just about a day ago. So Canvas, it's kind of similar.
Jordan Wilson [00:27:52]:
I I actually think it brings the best the best of both worlds between OpenAI's Canvas, which is more of like an interactive, you know, document editor that can render some code, along with, Claude's artifacts feature, which can render just like any programming language. Alright. So in this instance, I'm saying, I'm enabling Canvas, and I'm saying create an HTML clone of Wikipedia, but give it heavy Chicago vibes. Make it fully featured including clickable links and multiple pages that work. Make sure to include the most important Chicago things. Right? I'm I'm trying to have a little fun here, y'all. So, let's see if this works. So first, it is writing the code.
Jordan Wilson [00:28:37]:
So like I said, it's great at coding. Alright? Fantastic. Alright. So once it's done, which I don't think it should take very long, it's there's a preview tab as well. So when I, in, when I start this canvas mode, it kind of takes up the full screen, but I can, minimize it if I want. I'm gonna pull this over a little bit so I can see. Alright. It should be done here pretty quickly.
Jordan Wilson [00:29:07]:
As I take a sip on the coffee and I'm scrolling through the the the live stream comments here, y'all. I'm gonna see if there's, any questions. Alright. Josh said, look what I created this morning. Go go check out what Josh created. Charles says, why don't you use chat g p t for news? I do. I do as well. So that same URL, I did a whole entire show on how I did this, Charles, using chat g p d task.
Jordan Wilson [00:29:31]:
Monica says, what do you think are some of the best business use cases for this model? I still have a couple here, Monica, but I think one of the best ones working with PDFs. Right? This has been getting accurate information extracted from PDFs and then being able to use that as a baseline. Right? Because now I have all that text and maybe I'll do something with it that I extracted from a PDF. That's a a simple no brainer. Everyone's working with PDFs and, you know, essentially extracting any information from a PDF if you need to recreate it, if you need to grab some information from there and use that as a start for, you know, creating content. Right? So, in my example, I had, you know, our everyday AI kind of, sponsorship kit. I could then use that, copy and paste some of that information, go into deep research and say, hey. Are these, rates accurate according to twenty twenty five popular podcast or something like that? So, that's that's one small thing, one small thing I can do.
Jordan Wilson [00:30:28]:
Alright. Let's look at this. I'm gonna zoom out. All right, here we go. So we have our, let's, let's see. How can I do this? Full screen here. I had this last night. I thought I could.
Jordan Wilson [00:30:46]:
All right. Anyways, we have our Chicago Wikipedia, literally one shot. All right. So it says welcome to Shikipedia, your go to source for all things Chicago from a Chicago's point of view. Forget the encyclopedia. This is where the real info's at. So this is a fully functioning Wikipedia clone. Right? I can click oh my gosh.
Jordan Wilson [00:31:11]:
It works. There's multiple pages on here. It's interlinked. So I can click deep dish pizza. Right? And then I can, you you know, at the bottom, it says all see also Chicago hot dog. I can click Chicago Hot Dog. The Chicago hot dog, also known as Chicago red hot, is a culinary masterpiece in a bun. Right? No ketchup.
Jordan Wilson [00:31:31]:
Al, all beef. Right? This is so good. This is so good. It literally created a very small version, of Wikipedia, but Chicago style. And then the good thing is I can go in, I can go in and change anything with natural language. Right? And I can just say, you know, make it make it way more Chicago, and more nineties bulls references. Right? Whatever. Alright.
Jordan Wilson [00:32:00]:
So we're gonna come back to that one here in a second, and go on to our next use case. That one was fun. What'd you guys think? Pretty impressive. I thought, alright. Let's do this next one here. Okay. Here we go. I might not even have time, to read this because it's a little bit of a longer prompt, But I am essentially saying, you know, you're an analytics and research expert using Gemini 2.5.
Jordan Wilson [00:32:29]:
Analyze the sentiment of online mentions of Apple over the past thirty days, and I'm giving it kind of step by step instructions. You know, I'm saying essentially look at all of the information on the on the open web that people are talking about Apple. Right? Then identify five recurring themes or issues based on sentiment analysis. Right? Provide actionable recommendations for Apple's PR team to address any negative sentiment, and then ultimately, I'm using, Canvas for this. And then I say, create an interactive dashboard that displays your findings. Make sure to go into insane detail, ensuring accuracy and depth. Alright. So, I actually did this one, previously, in my first version.
Jordan Wilson [00:33:18]:
Okay. Let's see if it does tooth okay. Look at this. Gemini was was, a step ahead of me, y'all. It actually created two different Canvas files, within the same, within the same kind of, response here. So, okay. So it's building our sentiment dashboard. Cool.
Jordan Wilson [00:33:39]:
Alright. So first, here's the sentiment analysis over the past thirty days. So I wanna again, human in the loop, look for accuracy. This is correct. Right? It says AI strategy execution concerns. Alright. So this is good. It gave us a good text based report.
Jordan Wilson [00:33:55]:
It gave us actionable recommendations for Apple PR based on real time up to date information. It gave us five recurring themes. Right? Vision pros lackluster reception. Oh, weird. If only someone would have told you that six months before it came out. Oh, wait. I did. Alright.
Jordan Wilson [00:34:11]:
So it gave us a great text document, in Canvas. So, one thing you'll notice about the Canvas, if if you haven't used it, it does have some of those great, chat GPT, UI UX features where you can just change the length, you can change the tone, you can suggest edits. So I can just type live. Right? So it's literally like a Google Doc, which is very impressive. Right? Even just the Canvas integration from the text based perspective, is extremely useful for any business use case because right away, I can export this to docs or I can just continue, to type and work with it here. But it created two different canvases for me. Let's see how the other one turned out. Bam.
Jordan Wilson [00:34:52]:
Love it. It actually turned out not as good as my first I did demo this one first, but it gave me a very nice looking, kind of interactive dashboard. You know, nice colors. It says overall sentiment, mixed slash cautious. It says, while investor metrics, you know, example, alt index score of 64 of a hundred show underlying positivity, recent public discourse reveals significant caution primarily due to AI strategy concerns and competitive pressures. So a great job of just understanding overall sentiment over the last month of what people are talking about Apple. Is it good or bad? Right. It gave us a green column and a red column.
Jordan Wilson [00:35:33]:
Key positive sentiments, key negative sentiments, top five, recurring themes. That's great. I'm gonna try just one more thing here. I'm gonna zoom out and I'm gonna say I'm gonna say make, let me copy this. And I'm going to say, make this, more interactive and visual. All right. We'll come back to that. Let's go back and see, if our, Chicago Pedia got even more, got even more Chicago.
Jordan Wilson [00:36:07]:
Let's see. It did. Fantastic. Now we have a dedicated sidebar column for the teams, bulls, and bears, doubles. Yeah. I'm from Chicago. I love this. This is this screams out, like, you know, nineties Chicago.
Jordan Wilson [00:36:21]:
I love it. Says tall buildings and stuff. The lake, dibs, dibs. You know the rules? Yeah. Throw your chair out. Reserve your parking spot on the street. This Chicago pedia, I love it. Right? And the cool thing is, if you didn't know, the code is all here.
Jordan Wilson [00:36:36]:
Right? So, yes, you can render everything live, inside Google Gemini 2.5 Pro in the canvas feature. But if you did want to take this offline, you can copy and paste this. Sometimes it won't work. Just copy and paste because you might need to install some certain libraries. Sometimes it will, but it depends on kind of what languages are being used. This is strictly HTML. So I think in theory, I could just copy and paste this, put it on a website, and it would be good to go. And y'all, should should I publish this Chicago pedia? This Chicago pedia? I don't know.
Jordan Wilson [00:37:11]:
This one's this one's kinda fun. I like this one. Alright. Sandra already says she's gonna rewatch this episode. So let's see. Jackie is asking great question, Jackie. Can it get past logins on social platforms? No. So all we can do, with, you know, those different tools that Google was using to look at the web, that's the open web.
Jordan Wilson [00:37:38]:
Right? So anything on social media for the most part, is closed web. So even on Twitter, right, you're like, oh, everything's public. Well, you have to be logged in, because, you know, certain, there's certain restrictions specifically on social media that a lot of scraping, sites or, you know, tool use, kind of, tool use or Internet use tools from AI, large language models cannot pick up that information. Great question though. Love Chicago Pedia. Yeah. I do too. Alright.
Jordan Wilson [00:38:08]:
I have so many examples y'all, and I'm surprised that many of them are working. So let me scroll through here, and I'm gonna try to find, maybe something that's a little more impressive. Okay. Here. Here's one. I think this could I think this could be good. Alright. So I'm saying, let's go ahead and launch a new window here in Gemini 2.5 pro.
Jordan Wilson [00:38:30]:
Alright. I got way zoomed out. So I'm saying, create a visual memory game or interactive quiz that will help me learn and memorize this content. Alright. So then what I'm gonna do is I'm gonna go to the your everyday AI page. I'm gonna click on episodes. I did mention this, but you can go read, watch, and listen to anything on our website. So, you know, I'm going to our episode from Monday where we did the AI news that matters.
Jordan Wilson [00:38:57]:
Right? If you didn't know, you can listen to the podcast on the website for free. You can watch the video for free. We have a a little write up from some of the key points, you know, and then we have a complete transcript as well. Alright. So all I'm gonna do, I'm gonna copy and paste all of this information. Alright. I'm going back into, Google Gemini. I'm just pasting this, and I'm saying create a visual memory game or interactive quiz that will help me learn and memorize this content.
Jordan Wilson [00:39:24]:
Alright. I'm gonna click enter, and let's see what happens. Alright. So talk about business use cases. Right? How about making an onboarding fun? You you know, you have all these long, boring, onboarding docs. Right? Make a fun game out of it. Right? So this is what I'm doing. I love finding new ways to learn.
Jordan Wilson [00:39:45]:
I love learning with notebook l m, the audio overviews. I love notebook l m's new mind map feature, but I'm always finding new ways to learn. One problem with AI, it's making it harder for me to retain information. I learn way more per day than I did pre LLMs, but I also that means I forget more. So I'm always looking for new and better ways to learn and retain important information. So again, think you can use Gemini two point pro, 2.5 pro to automatically curate, you know, certain information that you might want to that you might want to learn. In this case, I'm just using, a transcript from a podcast. Alright.
Jordan Wilson [00:40:24]:
So let's look, see what it did. It's done. Oh, gosh. This is gonna be embarrassing. Alright. So it created a quiz. There's 15, 15. Hey.
Jordan Wilson [00:40:33]:
You guys wanna do the first couple questions, livestream audience? Alright. Let's just do a couple questions together. See if you tune in. Let's see if you tune in Monday. So it says AI news quiz. It it just, for you all, this looks pretty good. It's got this kind of purplish background, very like web two point o. There's hover animations.
Jordan Wilson [00:40:54]:
It's pretty slick. It looks nice. It's not some ugly, janky, you know, nineteen nineties looking quiz. It looks really good. Alright. So, livestream audience, let's play along. We'll just do a couple questions. So it says the deterministic aspect mentioned in Microsoft's agent flows aims to reduce issues like is it high cost, hallucinations, language translation errors, or slow processing speed? What do you guys think? I'm gonna take a sip.
Jordan Wilson [00:41:24]:
Alright. I'm gonna guess AI hallucinations. Yay. It said correct. Cool. Alright. So it works. That's the thing.
Jordan Wilson [00:41:31]:
I just one shotted an interactive quiz based on, I don't know, couple thousand words, and it took, like, a minute. If this doesn't change how you think you and your team can interact with you, even your own internal docs, I don't know what else to say. Next question. Live stream audience. Who's gonna who's gonna get it first? Alright. Uh-huh. This is meta, but not meta like Facebook. Meta as in we're using Gemini 2.5 pro to ask about Gemini 2.5 pro.
Jordan Wilson [00:42:01]:
What key feature allows Gemini 2.5 pro to process extremely large amounts of text, audio, images, and code. Oh. Oh, this one's, this one's a little tricky. So cross layer transcoder, deep reasoning agents, deterministic logic, or 1,000,000 token context window. This one's very actually interesting because it didn't just make things up. The wrong answers, which hopefully I get this right, livestream audience gets your vote in. The wrong answers are actually, key terms from other announcements from Claude and from Microsoft, but we're asking, about, we're asking about Gemini 2.5 pro. I believe it's the 1,000,000 token context window.
Jordan Wilson [00:42:47]:
Oh, good. I got it right. Alright. Let's do one more. Alright. It says OpenAI is reportedly nearing a funding round of what massive amount potentially led by SoftBank? Okay. This one's actually a little tricky because there's, a total amount. So is it 40,000,000,000, 10 billion, 20 billion, or 33,000,000,000? There's actually a total amount of funding, and then there's a certain amount of funding that SoftBank is reportedly on the line for.
Jordan Wilson [00:43:11]:
But that's actually two different amounts. One amount is if OpenAI does successfully transition from a nonprofit to a for profit, and the other amount is if they don't. So there's technically three terms, a total fundraising term, SoftBank, a, if they do convert to, for profit, b, if they don't. So the question is OpenAI is reportedly nearing a funding round of what massive amount? The amount of the funding round is $40,000,000,000. Alright. We got it right. The cool thing is I can say something like make it more, you know, make it even more interactive and, detailed, maybe some slight animations, make it look and function better. Right? That's the coolest thing.
Jordan Wilson [00:44:02]:
I didn't write a single line of code. I don't need to. I can control this with just natural language. Like, yo, LLM, make this better. Make it shinier. Make it blue. Make it harder. Make it easier.
Jordan Wilson [00:44:15]:
Make it for pros. Make it for amateurs. Right? Create a graduated model. Right? First, you know, give me 10 questions that are much easier, than, you you know, help me level up or, you know, turn it into more of a video game. Right? There's so many things that you can do. Alright. I'm gonna give this a second, to finish. Let's check-in.
Jordan Wilson [00:44:35]:
Oh my gosh. Look at this y'all. So our our Apple sentiment analysis, remember, I just in natural language, what did I say? I just said make it more interactive and visual. It improved it by a lot. So there's there's some things that didn't, fully render. Right? So there's some code that says, like, more rounding. But overall, it made this look much, much better. It gave it kind of this these these gauges and barometers with, you you know, certain, you you know, filling.
Jordan Wilson [00:45:07]:
It just made it look much better. So these are toggles, little toggles, even though there's not a lot of information in them. So really good. Really good. Alright. Let's see. Alright. It's already done.
Jordan Wilson [00:45:22]:
Our our our news quiz is done. It added, a status indicator. Okay. Now now it's actually hard. I don't know. What episode number and date was featured in the AI news summary? Oh, gosh. Without looking this up, what was it? I think it was 493. Oh, good.
Jordan Wilson [00:45:41]:
I got it right. Okay. So okay. Unfortunately, the the the status indicator did not light up, but I could change that. Alright. So, very impressive. Should we do one more, y'all? Should we do one more? Should we wrap this up? Let me know. You guys you guys all got this right.
Jordan Wilson [00:45:58]:
I'm looking at our at our live, at our live comments. You guys got it right. You you must have all watched this episode. Alright. Alright. You guys said one more. Let me just go ahead. Let me see if I can get something that I think is maybe impressive.
Jordan Wilson [00:46:15]:
Okay. Cool. Let's do this. Alright. We're gonna do one more quick one here. So this, you know, we talk about use cases. I just randomly threw one out. I'm like, how about you make your internal documents a little better, a little more fun? Right? So here I'm saying, essentially, you're an HR expert using Gemini 2.5 pro.
Jordan Wilson [00:46:45]:
You know, hey. You you you work at IBM, create a a a manual with standard operating procedures, for new employees. So, essentially, I'm saying create an onboarding form for new employees at IBM, and then also, you know, an eight question quiz that covers key SOP elements, ensure all recommendations are based on real IBM training methodologies. So I'm wondering if it's actually gonna go pull this and find this information from the web. I guess I'll have to verify this later. Right? Just because, human and expert in the loop doesn't mean I need to do that live. Right? I'm not gonna, post this and say it's, perfect and working. But you'll see already.
Jordan Wilson [00:47:27]:
One thing I love that's a little different with the Canvas inside Google Gemini versus, some Canvas, features or functionality in OpenAI or, anthropics artifacts is it can create multiple, kind of canvases. Is it canvases or Canvite? Alright. I think it's canvases. Multiple canvases at once. So, the first one is just this, onboarding material. Okay? So it's it's creating a, an SOP, with preboarding day one, week one, role clarity, compliance, and ethics. Right? So it's doing the kinda, like, boring. Right? Alright.
Jordan Wilson [00:48:02]:
Here's your here's your text based, you know, content here. And then this probably won't be done yet, but it's, let's see. There we go. It's already done. Okay. It created an interactive version, of this simple, you know, 10 step SOP onboarding, for new hires at IBM. Right? So it has our onboarding SOP. It's interactive.
Jordan Wilson [00:48:28]:
It has these tabs. I can click weekly tasks, week weekly schedule and tasks, and there's toggles with drop downs. Here's week one foundations and setup. Week two, role clarity and tools. This is really good. It's all interactive. It works. And here's a quiz.
Jordan Wilson [00:48:45]:
Is this quiz? I don't think the quiz is gonna work. Let's see. What is the primary focus during the first week of onboarding at IBM leading a lead leading a major product, completing essential compliance training and initial setup, presenting a strategy report senior leadership. I'm gonna guess it's the middle one. Alright. So it doesn't say unless I have to, like, click okay. There is a thing to submit the quiz. So I'm just gonna click one.
Jordan Wilson [00:49:08]:
I'm wondering if it's gonna tell me what's right and wrong or give me a score. This would be very impressive. A multi step quiz embedded inside an accordion. Alright. So it didn't, tell me which ones were right or wrong on this one, probably because there's no database. And then it has a checklist as well. This is very cool. So this is my onboarding milestones checklist.
Jordan Wilson [00:49:32]:
Right? And when I check it, it says two of 10. I check one more, three of 10. Let's see what happens when I finish it out. Boom. Says 10 of 10. Very impressive y'all. Alright. We covered a lot.
Jordan Wilson [00:49:44]:
I know this episode was all over the place when we talk about different use cases, for Gemini 2.5 pro. So I'll say this. It's not perfect. Right? It's not perfect. The ceiling is high. The floor is finicky. Right? But as long as you, the human, you, the business leader out there are paying attention, are being patient, are properly prompting, Gemini 2.5. And, you know, you might have to dip your finger a little bit into Google's AI studio.
Jordan Wilson [00:50:15]:
Extremely, extremely powerful state of the art multimodal, multifaceted, large language model in Gemini 2.5 pro. And the use cases are tremendous. Right? It's it's, actually baffling how many, even just what we went over here live. Right? I didn't really plan these. I didn't refine them. I wanted to give you guys just just the the nitty gritty. Right? Let's see some mistakes. Let's try to improve it a little bit.
Jordan Wilson [00:50:47]:
But, if your brain isn't churning, if one of these didn't hit home, you gotta check you gotta check for a pulse y'all, because what we just showed in this one quick episode podcast audience, I'm sorry. I know this one was a little bit more visual. I know I didn't do a great job at, you you know, describing everything, but, you know, make sure you go watch this one. But if you didn't get at least one idea on how your business, how your role, how your department can fundamentally change by using Google Gemini 2.5, you gotta rewatch this because it's in there. Right? So think, what public data do you have? How can you make old documents? How can you bring them to life? Right? In the same way that we talk about large language models becoming multimodal, right, I think businesses also need to start taking that same approach even for their own internal document. We don't live in, like, we don't live in a text based world anymore. Right? We can create games. We can create interactive quizzes.
Jordan Wilson [00:51:49]:
We can create, you know, visualizations and business dashboards now with zero coding knowledge. Right? Before, you might have to have a team of developers, some people in BI. Right now, you can just copy and paste. That was one of the things I wanted to do, but we ran out of time. Copy and paste a bunch of data, create a a a business dashboard. Right? And you're off to the races. Right? You already have ways that you can instantly, use generative AI to grow your company and your career. That's what it's all about.
Jordan Wilson [00:52:19]:
Alright. I hope this one was helpful y'all. Part two, again, maybe you just listened to this one for the first time. Make sure to go back one episode. Listen to part one where we go over more of the details, the bullet points, everything that's kind of under the hood, how the model works, all that. But hopefully, in this, live demo example, sometimes they work, sometimes they don't. I hope this was helpful, and I hope this is sparking, some some some ideas in your brain on how you can use not just Gemini 2.5 pro, but just large language models in general. Right? If you're not already using, generative AI in large language models day to day for every aspect of your business, you've got to rethink how you are working.
Jordan Wilson [00:52:57]:
You need to rethink your role, rethink your department, rethink your company, rethink what it means to be a knowledge worker. That's what all of us are. Alright? So it starts here, but you need to go to your everyday a I Com. Sign up for the free daily newsletter. We're gonna be recapping, today's post. You know, if if some of y'all shared some examples, maybe I'll I'll throw one in the newsletter as well. So thanks for tuning in. Hope to see you back tomorrow and every day for more everyday AI.
Jordan Wilson [00:53:22]:
Thanks, y'all. And