Combining AI Tools for Better Results

Key Takeaways

Today I'm very excited to be joined by Grace Learn, spelled L EU NG. And Grace is a digital growth consultant who helps marketers grow organically with AI strategies. Her community and newsletter is for marketers and entrepreneurs who want to strategically grow their businesses with AI. And you can find her on YouTube at Grace Lu N Y L. Grace, welcome to the show. How you doing today? >> I'm good, thanks. Thanks for having me, Michael. >> I'm super excited that you're here. Today, Grace and I are going to explore how to combine different AI platforms and tools to get better results. Now, before we go there, I want to hear your story. How did you get into AI? Start wherever you want to start. >> Yes. Um, yeah. I So, I first uh I started my career in this field back when I was in Asia. I I have I've been in the digital marketing field for over 10 years until three years ago I moved to Canada um with my family and then since I moved here I joined a SAS company and then leading the growth marketing programs for the new product and then um that time I still remember you know uh 2022 late 2022 when Chad first came out and then I still remember uh a day I my colleague shared uh chatbt through Slack. Okay, try this. Please try this. It's so amazing and okay, let's try it. Oh, my mind was blown because it's so powerful, right? Because you can just ask anything to try GBT and then it will give you response. So that time I already see oh there's a huge potential how this thing we change how we do marketing and so I started experimenting more and more maybe like dropping social media poses like um creating ideas brainstorming ideas or even maybe comes up some persona details for our campaign add copies and and then I just try more and more. So I think um this is a real game changer and and I think is AI has been in the industry in the marketing industry for a long time right like Google Facebook has been using AI to uh do ad targeting recommending the the ads but however I think chat GBT uh was so powerful is that it's become so accessible. >> Mhm. >> It makes AI so accessible. It's not just about certain group of people not just data scientists but also non-technicals like me as a marketers to everyone and so I think uh although now looking back it may feels very basic but that time imagine right as marketers we need to do research ourselves we need to prepare the copies ideas so I think the the transformation impact was so huge >> so tell us about bring us up to the present with your story. So what are you doing now? You're still working for that company or have you gone out on your own? What are you doing today? >> Yeah. So to continue that story, I think at the same time that time I started my YouTube channel because I think okay I just started as a passion project. I just think okay maybe in this new chapter of my life I want to try something new. So I just start to share whatever I know about digital marketing and then I also start using AI because I I found this is super powerful and that time more AI tools came out like I still remember those days maybe uh content creation tools like copy.ai AI, Jasper, Rice, Sonic, and then I started doing more reviews and then Perplexity came out and then German high and then claw. So, it's like a you you just go down to the rabbit hole. What's next? What's next? And then you keep sharing on YouTube. And then until I pivot from the corporate to now um as a independent growth consultant, I use um AI more and more for my own business and also for my clients because I think two years from now has changed a lot. It's not just no longer just content creation. You can do a lot with AI strategy, charts, um visualization, image generation, video generation. So it yeah the the the the impact just is is huge. Yeah. >> Awesome. Well, thank you Grace for sharing that story. So many people listening to this podcast, whether they be marketers or entrepreneurs or creators are predominantly focused on just chat GPT, right? Because maybe that was the first tool they started using and or it's the one that everybody uses. But why is it important for people to expand their horizons beyond just chat GPT? >> Yes, that's a good question. I think um that's uh it's very natural. Most people start with Chad GBT like myself and then like stealth taught and then try to experiment with Chad GBT but however as as time goes on as AI become more advanced you can see that the more AI models coming out like for example Gemini maybe the first version B is wasn't super impressive but however like Gemini has been catching up and then I would say different AI models have their different uniqueness and strength because the difference um they are trained so differently. So for example, Chad GBT is a test based model but however Gemini has been trained at multimodeling since the very beginning. So that's why Gemini can handle tags, images, audio, videos all together at the same time as input and it's not it's not a problem or maybe claw is trained at more uh focus on linguistic and also having more consideration and human tone. um ethical consideration. So you can see different models will start to develop their own capabilities. I'm not saying that as marketers you need to use different models but however you need to know that um the strength so that you you're not missing any opportunities. Maybe certain AI models is better fit for your certain task. So you need to um be more open to test more and also maybe uh yeah test more and not just rely on those benchmarks. >> Yeah. >> Yeah. And um I'm going to double down on what you said um you know Gemini by Google because it's multimodal as you said it's it's got eyes. got ears and it and it can not just read, it can hear and it can see and that's really important and chat GPT has is evolving in that general direction but there is something to understanding that a model that was just trained on text is not seeing the whole world and all the context that's out there where models that are trained to see things and understand things and listen are going to have a different perspective because they are able to do things the other models cannot. So um that's kind of setting a little bit of a groundwork stage. So for those who um want to expand their horizons beyond just chat GPT, what do they need to be thinking about? Um what are before we get into kind of some use cases here? What are some of the things that maybe they need to consider? >> I think so first is have a strategic or structure testing framework. You need to identify what are some recurring tasks for your day-to-day work and do not start with different so many different use case. So for example maybe your use case is about strategy work and then you pick the recurring tess and then you design the prom and then you try the same prom on different models at the same time because that would give you the most objective way to test the model ability. So you want to test the speed like how fast is it responding the accuracy if there any bias or inaccurate information and also the tone right how they they present output because I would say for some model maybe cla I use claw a lot and is always have that kind of human touch is always comfortable to chat with while maybe for chat GBT is more >> logical is more subjective and then yeah this is the Right. So, uh you need to test it out and and understand if this is the right tool to fit the task and then the second step is to map out the workflow. Okay. So, if I decided maybe perplexity is better for research then I will use perplexity as my go-to tool to do all research task. maybe after perhaps maybe in my ca case I always use it for strategy work and then I always love claw uh visual storytelling more strategic so I will use that as my go-to tool for doing this kind of tasks so you need to first understand what is your recurring task and then map out your workflow and understand if any bias and one tip I have is perhaps maybe you can just ask it some factual question for example um I let's say I just mix this up maybe I'm the marketing manager of notion and then I will ask like give me what you know about this brand like give me the brand overview product uh messaging everything and then so you can compare it if there any bias if any gap between different models and then you can also study uh their reasoning the uh chain of thought thinkings right because you can see there's uh some chain of thought thinkings and how different models will um break the complex tasks into multiple step because that will also tell you a lot um how different models will approach different tasks and you can also spot okay if this is making sense or not and so you can decide which one you want to use it more. >> I love this. Um I'm going to reiterate what I heard you say. Number one, >> have some sort of a structured framework. Yeah. >> Um, identify some actual applications that you're already using internally and once you've got a prompt, take that prompt and try it out with another tool like Gemini or Claude. Um, and then just notice whether or not you feel like it's uh giving you a response that's a better response. Uh, maybe it's faster, maybe it's the the way it's communicating is more preferable to you. And then specifically, you said look for biases, right? and to look for biases. ask it things that you know the answer to >> maybe about your brand or your business and then see which one is more accurate that might give you insights that might be more useful >> and then of course um map it into some sort of a workflow right so once you begin to identify like okay this one here seems to be better for X like I'll say for my own case I use claude nearly exclusively for uh writing uh persuasive content you know and I use chat GPT to come up with very simple ideas and I know that chat GPT is good for coming up with ideas. Um, also I'm working on a survey and I use Claude to help critically analyze the survey and then I took it over to um uh Chat GBT3 and and it actually found things that Claude didn't find. So I actually used both both models. I used Claude 4 Opus and I used uh Chat GBT3 which were the two most advanced models at the time of this recording for each of these and I found that each of them found things the other did not and had preference differences and I was able to go back and forth and just kind of like um use both of them and each of them had a little value that they added to the equation but it but I and it resulted in a better list of survey questions that I would have never had if I was only using one tool. Right. >> Yes. Yes. I love it. Yes. So that's exactly why we need to use multiple tools and especially when it comes to important work, strategy work where you just want to have more diverse perspective. You just don't want to get the same response from one model. So I think this is yeah the exact exact. All right. So um let's we come up with a couple of different um use cases that um we're going to talk through in detail here and um let's start with analyzing uh large sets of data. Now before we explain how to do it, why don't you discuss like what do we mean by analyzing large sets of data? Let's kind of define a little bit about what we need and what we mean and why we might do this because some people may have never done something like this before with AI and then we can get into the how. So let's describe what we mean by analyzing data sets and then let's talk about how >> so data sets means it it will consume a lot of tokens maybe it maybe it just is a large reports have over few hundred pages is like consume a lot of tokens and to give you idea um for example Gemini can take up around 2 millions tokens is around 1.5 million words so it's a lot so many words and is in terms of tokens it can be is um data sets or it can be a reports with so many pages. So here's how we define it as a like large data sets or files >> and what might we do with those large data sets and files like let's just talk about the application a little bit and then we can talk about um you know like how can AI assist what would it be doing in this particular situation >> yeah so actually this is one of my favorite use case because AI is so good in analyzing patterns it's so good in analyzing un turn unstructured data into structured format So for example, you can just copy um and paste maybe YouTube comments or maybe forum reveals uh my business profile reviews and then just paste it to Gemini and then I say okay h help it to to make it more structure maybe present in a table so I can download it for further analysis. This is one of the ways you can do it. Another way is you can just directly ask it to give you the insights from these structured data and then help me to grip by themes. what are some common themes you can see from these large data sets. So I would say um this is a game changer because most people just jump directly asking AI for recommendations but however AI is just so useful in finding linkage and insights from from from those unstructured data sets. Okay, so we're going to talk about how to do this in just a second, but uh the two kind of examples we were talking about is possibly lots of files, for example, PDF files, right? Like you could have a whole bunch of different files or you could have one really huge file, right? Um or you could have unstructured data like a bunch of comments, right? So you could have a whole bunch of comments that you could take off of a YouTube video, for example. Maybe there's hundreds of comments in a YouTube video, right? And that's an example of unstructured data. And you get that data somehow into a Google doc or a sheet or something like that. And then when you have all these uh files, right, large files or multiple files or large amounts of data, what do we what you said, the beauty of AI is it can help you discern insights and stuff from that. So talk to me about how we would use the different AI tools together to kind of create something powerful. M so one of the use case is for example audience research right I believe most marketers need to do audience research and so let's say I have so many data SAS large maybe reviews comments or anything maybe reports you just put it um to Gemini 2.5 because Gemini the beauty is it can in intake um a large data size with is 1 million um tokens window size and then you can ask it to give you the data summary. So it's not necessarily ne not necessarily the the insights but however the data summary which means that what are some patterns you can see what are some words or phrases that is used a lot and then you can ask it to do some statistical summary analysis just a initial analysis and then you can fit this analysis back to claw. So why I use I prefer claw in this use case is because I always find that claw is um a bit more strategic and also when it build a dashboard is it present the story better. So I can just put this data to claw and then ask it to generate the dashboard maybe like based on these analysis come up with um two to three key persona details. So what what are the thing you identify what are the common pain points and then what are the motivations perhaps maybe you are starting a new SAS product maybe is a email marketing product so you have you have Gemini to do the analysis and then you have claw to define the persona for you so that or maybe even you can take even further help me to recommend some messaging that would engage with these three target personas based on the reviews you analyze the theme you analyze what matter the most to these target personas. So the this is uh one of the most useful way in in using them together. >> Yeah. >> Okay. A couple quick things folks. Um Gemini is if you have a paid Google account then you have gemini.google.com and it's important. Why don't you distinguish between 2.5 flash and 2.5 Pro? And this could change in the future, but explain which one is the better one for doing this analysis cuz um you know it might be 2.6 or 3.0 by the time you listen to this in the future. But but when you go into Gemini, you have typically options. Which one should they be choosing when they're doing um this initial analysis inside of Gemini? The Flash or the Pro? What's your thoughts? um I would prefer pro for all kinds of um data heavy analysis. So in in in the case we just um discussed like audience research I'll definitely prefer pro while fresh is more uh about quick response it's more lightweight so you just want to get ideas quickly get response maybe drafts use some content is good but however for 2.5 pro is more um about data heavy maybe coding maybe um some other tasks that you need to do a um uh more through analysis. So I was this is how I would like pick between the two. >> Okay. Now when we take the output that comes from Gemini 2.5 Pro, are we just taking the output over to Claude or are we taking the output and the original data over to Claude? I'm trying to understand like where we go that step from going to from Gemini over to Claude. I will just take the output generated from Gemini to claw or maybe if you prompt Gemini you can just let Gemini knows that okay these analysis uh the output I'm going to pass it to another AI to do more in-depth analysis. So it also helped u the uh Gemini to generate more useful output. So I would just use the output generated and then pass it to claw and not just not the original data set because the thing is uh claw has um straight usage limits and also can't handle super large data set. So that's why in this case that's the reason why we want to use Gemini to do that like dirty work initial analysis before we pass to claw for more attention to detail strategic work. Yeah. Now, Claude, I'm a heavy user of Claude just like you are, and they have Sonnet and Opus, and as of now, I believe it's 4.0. >> Which one do you recommend uh when we're doing this kind of work specifically inside of Claude, Opus or Sonnet, or do you find it doesn't matter? >> Um, I use Sonic more to be honest. I've also tried Opus, but I would say just from my personal experience, um, the the token was just burned so fast. >> Yeah. with Opus in particular, right? >> Yes. Opus. Yeah. So that's why I believe um and also from the official guide uh from from claw the the recommendation is if you're doing heavy coding um website development landing page building stuff maybe you want to prefer using office but however maybe this kind of analysis you can just use sonic is is smart enough and yeah >> now claude for folks that don't use claude has this really cool thing called artifacts do you recommend artifacts at all when you're using claude and if so describe a little bit of how that works because I think that's really cool right >> yeah so artifact is just like um a prototype that you can iterate like in different versions so you can just say okay generate an artifact and it's working you can maybe build tools uh so there are so many ways you can use artifact maybe you can build dashboard maybe you can just build a lightweight tool maybe an SEO tool or a calculator so this is just so useful like And I would say this is one of the best way to use claude. Yeah. >> Well, and claude also artifacts will format it in a beautiful format for you too. So if you want it to be having different headers and bullets and you want it to be nicely formatted, claude will almost look like a Google doc and then you can just copy and paste it. So it allows you kind of to see what it looks like before you actually, you know, like normally with these models, it's not formatted beautifully, but we'll put in when when you put in artifact, it can make it look much nicer, which is I really love. Okay, so we've talked about this first application. Any other any other tips on um moving from large sets of data, doing the initial research with Claude, I mean with Gemini, and then moving over to Claude? Is there any other little things that you've discovered like for example do we accept Gemini's first output as the truth or do we want to modify it and tweak it a little bit you know when you're doing that initial analysis with Gemini >> yeah so I think Gemini is good for doing all those rapid prototyping initial just all those dirty works and then you can just pause it to cl maybe to the do the finetuning >> uh >> and so for example maybe you are building some landing pages or um building the tools. You can just ask Gemini to build a prototype and then you can just pass it to claw to refine it because the the the pain point of using claw has always been the usage limit. >> So you want to always have Gemini to build u the groundwork the framework for you before you pass it to claw. Another thing and use case I think of is because Gemini is multimodel so you can ask it to analyze maybe videos maybe audio and then you can analyze it um pass it to claw to refine it because claw is so good at writing doing the style guide and so you can ask it to generate the style guide and then you pass it back to Gemini to maybe generate the audio because um the multimodel capability of Gemini is is is is real. Yeah. >> Okay. Uh that note, all right. Do you recommend uploading actual audio and video files into Gemini or do you recommend linking to videos? Um which is if you want to get audio and video into Gemini, what's the best way to get it in? >> Um from my experience, uploading is always the best. Uh the Yeah. Yeah. Yeah. You will ensure the context is doesn't lose the context. But um for Google ad studio there is a native features offered that you can import the YouTube videos. So for example is the video is already on YouTube you can just um import it um so easily. Yeah. And it will do all those analysis. It's so crazy because Gemini can like detect the pause the pacing the structure if you have even smile throughout the videos. So you will get a lot of um really good analysis perhaps maybe you're um doing some presentations you want to improve that or any other um on camera presence you want to improve that so you can use Gemini in this way and then before passing the club yeah >> so we should just mention because you mentioned AI studio for those that aren't familiar AI studio.google.com Google.com is more of their like um engineering technical interface. It's a little more complicated to use. I can speak from my own experience. I've had mixed results with YouTube videos. Um I think it depends which model you pick. Um I've had I've given it videos that I've published and it didn't even it couldn't even read it. So I don't know if you've had success recently. I I think part of it has to do with which model you pick because right in Gemini there's a lot of different models, is there not? >> Yes. Yes. a lot like like maybe date like in June and then in in May and then so but but I but I always my preference always pick the latest the latest version. It's yeah so far I haven't encountered the same issue as yours. >> Okay. Well that maybe they've changed that. Okay. Perfect. >> Okay. So now let's pivot to research. I know we've talked about research but this is a different kind of research. Let's assume we're doing internet research. Um let's explore the kind of tools that we might use um to help us do that and maybe talk about some specific kinds of research that we're talking about here. >> Yeah. So for research um I would say there are few types of research like everyday research just get quick answers or maybe a more in-depth research and then also maybe researching from your closer knowledge base. >> So I think nowadays I use different types of tools for everyday searches quick searches I use perplexity the most. for deep research I use Gemini more and for maybe I just want to deep dive into the sources that I chosen um just this knowledge base and then I use Noble LM because I think it's such a fantastic tool in so many ways and I would say and it all comes back to the to the recurrent tasks that we talk about like what are the most research needs for you so for example um perplexity I I always think it has a good research um source source variety but however Google may be prioritize more bigger brands or maybe uh sources that have high authority so you need to try that yourself so sometimes maybe I just want to g sources maybe I'll prefer perplexity and then I will just pick the sources that I got I select them and then I will import them to no LM and what makes no LM so useful these day is because It also just recently uh launched a bulk upload feature. So you can just ask perplexity to okay to export the list of sources for me and then I can just pract and do the bulk import. So it's it's very very handy and so you can just combine different tools and then ask LM question. >> Okay. So let's dig into this a little bit more. Um let's start with perplexity. Um, perplexity has been around for a while, but I think what I heard you say is use perplexity for quick answers. Now, give us some examples of how you would use perplexity um, and what kinds of quick answers because many people listening right now might be just using chat GBT um, for quick answers instead of perplexity. So, kind of explain in more detail kind of the advantage that perplexity brings to the table. M um okay so for example I am planning the go to market for certain brands or maybe categories maybe uh a e-commerce brand >> and then I want to know um the top competitors or top market players in this field so I can just this is an example of quick search what are some top players in this field give give the the the details for me so I can dig deeper and then I would just select them um to no um pick them to no lm. So for example, I am I want to enhance the messaging and then I'll just ask perplexity give me the top 20 maybe 10 to 20 like top players and then I will bulk import all the URLs to Noble LM and then ask no LM question. Okay. So what are the content gap? What are the messaging in all these like top market players you have seen and so and I asked them in case I also want to build and all the new product so how would you recommend for me to do the enhanced messaging and the beauty of NOM is the minimal level of hallucination. So it will al always based on the sources that you import before you give you the answer and and nothing more. Yeah. >> Okay. Couple questions on perplexity. For those who've been listening to the show for a while, understand that we're not supposed to use AI like a search engine, right? And the prompt matters, but what about perplexity? Should we use very simple queries with perplexity? You know, just give me the top 20 competitors or is it make sense to give it a much larger prompt? Do you understand what I'm asking you? >> Um, >> with perplexity. Yeah. >> Yes. Um, I would say if if you're just doing some quick analysis, if you just want to quick answers, you don't mind the prompt engineering. Yeah. Just make sure you get Yeah. you you get your objective so clear and you communicate in a way so clearly. But however, maybe you are doing deep research. You just want to have some focus, research focus, prioritize what kind of sources, then you want to have some more detail prom or maybe this day perplexity has just launched perplexity labs which is research plus building and also there is a quot uh of how many credits you can use uh for perplexity labs. So >> is it labs labs? Is that >> labs? Perplexity labs. >> Yes. So that's another story. So because you have limited usage and also because it's a building it's an agent agentic thing so you want to have more detailed prompt maybe you give it more directions what are the things you wanted to build or maybe you using the deep research mode what are the sources want to prioritize um the research focus and a tip is a tip for me is if you were doing deep research with perplexity not just getting everyday quick searches You can actually use a reasoning model maybe charge 03. You can just ask it to give you the research plan first before and then you pass it to perplexity to execute it. So this is also my favorite way of like combining. >> Oh wait. Okay. So hold on a minute. I want to make sure I got that. So I think what you said Grace is that you could go into chat GPT and use one of the reasoning models like 03 which right now is one of the more advanced ones. Yeah. and ask it to give you um do some of the preliminary research and then put that into perplexity. Did I hear that right? >> Yes. Yes. So you you just so you >> how would that what would that look like? I mean how would that result in a how would that result in a um different prompt if you will in perplexity because I thought you said keep it simple when you're putting uh prompts into perplexity. >> Yeah. So uh because of perplexity you can you can do the deep research but however if you want to make sure it is high quality. So what I so what I prefer you what you can do is if you also don't want to craft the prom yourself you can actually ask AI an intelligent AI like tragic 03 to craft the research uh the prom >> but not do the research but just do the prompt is what you're saying is that >> yes the prom. So I'll put the research plan in in a prom in a prom format. >> So I can pause it back to perplexity because what I found is perplexity of course you can give it more detail prom but nowadays AI is more become more advice. It is better um uh in crafting the prompts. I can definitely see this this trend. So you can just ask it to give you the version and then you fine-tune it. So it will give you more structured prompt like the research focus sources like a uh how you should approach this research >> um besides you just let perplexity to to do them themselves. So I would say this is a way to improve the quality um from the research. >> Okay. Couple questions about perplexity then I want to come back to this deep research stuff. >> Are you using a free version of perplexity or a paid version of perplexity in this research? um paid paid version. Yeah. >> What's the advantage to the paid version? Um so for the paid version you have unlimited quick searches and then also for deep research you you also have unlimited deep research and then for perplexity labs it is kept around 50 search uh usage per month but however for the free version is much more limited uh maybe I I remember it's like five to 10 free searches per day. I I'm not sure, but there is a there is a usage limit basically. Yeah. >> And how much does it cost? Because a lot of people are using the free version of Perplexity. Do you know how approximately what you pay for Perplexity? >> Um $20 per month. >> Okay. And does the paid version allow you to select which model um you're going to because doesn't Perplexity offer different models like doesn't it allow you to run through the API chat GPT claw Gemini or is that not true with Perplexity? Um so perplexity uh it allows you to change the model it has the it has the features allow you to to generate your own API key but however it doesn't give you options to use your own maybe API key from claw it it doesn't work this way >> okay >> but however it has um just think of it like a AI wrapper it's it just have like a middleman and then it can connect to different models okay which model is your favorite and then you can just pick it um through the interface. >> When you're doing research in perplexity and you want to grab those 20 recommendations, is there an easy way to get those other than opening 20 tabs and copying them all? >> Do you understand what I'm asking? >> Are you saying >> like when when you're doing perplexity quick answers, right? And you want to get like who are the top competitors in this industry? >> How do you get all that over to Gemini for the deep research? Do you have to like have each URL copied into Gemini? Do you understand what I'm asking? >> Okay. So, so what I usually do is I will export it to a markdown file. >> Okay. >> And then I can open it and then I can just um so from the exported file there is a citation format. >> Okay. >> Yeah. Because perplexity we include that in the exported file. So whatever format you choose maybe markdown maybe uh a Google doc maybe PDF and then what I would do is I will do a little u reformatting I would just ask maybe tragic or gemini okay help me to remove the number so I can just directly and ps it back to no lm >> okay interesting yeah >> so deep research um as of today pretty much everybody offers deep research claude has it >> gro has it they call it deep search um chat Chat GPT has it. Gemini has it. Um why do you like Gemini um over the others? Even Chat GPT has it. So what's your thoughts on why Gemini for deep research? >> Um so first thing is Google is is the best of doing search right although it's now pivoting like from the traditional search to search but but like this is what Google's do the best is always search. It has a large database and also it has um like not just website but also from YouTube. So I always think that um so you can so like what I mentioned you can try the same prompt um using both chb deep research or gemini and I always find Gemini can uh find much more sources >> way more like like on average maybe few hundred >> like sometime even over 500 websites and it's it's so it's crazy >> but it is also it generates a massively big file though I mean right like this is the difference right like I found with chat GPTs deep research the answer is a little bit more precise >> where with um uh Google it's like a monster document that I have to page through right is that good or is that bad I mean I guess it depends on the application right >> yes it depends on the application and and other thing is um I would say Gemini is will give you a more overwhel maybe you are not familiar with some new topics in AI or new subjects so Gemini deep research is so good and I would say is more generous in terms of the usage limits. So actually it has a usage limits but however it doesn't officially say how many the research you can run per month but however on charge GBT uh because I'm on a pro plan. I'm not a I'm not I'm on the plus plan. I'm not on the pro plan. Yeah. So so that's why uh the you have to very cautious about the usage. But at Gemini there's no such thing. And also um I I would say also yeah depends on your tasks. Yeah. >> Okay. You mentioned for internal research notebook LM. So explain that application so people can process that a little bit because I think we mentioned it but maybe very briefly. What do we mean by internal research and what is it about notebook LM in this case that makes it different than the other stuff we've been talking about? >> So so first of all notebook LM is a Google product is powered by Gemini. So that's why it h it also um has u a big context window. You can upload 300 sources uh if you are using the the Noble LM plus version. So you have you can just upload so many sources to it. And the beauty is it will only um find the it will give you the response based on the sources you import and nothing more. it will and so the hallucination can be minimized and so one way is you can so for example nowadays uh Google is doing uh emphasizing more AI search so one way you can just ask it to discover the source this is also a new features from no LM because in the past no LM uh you cannot use it to find sources you have to import them one by one yourself but nowadays it has the discover source. So that's why you can just describe the topic and then it will find the source for you. And the beauty is because it's powered by Google. So it's somehow also leverage Google's ranking or how Google priorize the websites. So you can kind of reverse engineer how Google thinks what are the quality websites. So for example, if I am uh I want to reverse engineer the ranking for uh maybe uh a project management tool like what is the best man project management tool and then I submit the query in notebook LM and then it will feedback the 10 top uh sources for you. So these why these 10 top 10 10 sources is because Google think that they are high quality right they have >> oh so those sources are articles written by somebody else presumably right is that what you're saying >> yes yes so it would come up the sources so it's just like the the ranking in the Google traditional search result page but however now translated to no LM and AI tool and then I can just import it okay these are the 10 sources I upload to no LM import it and then I can be reverse engineer. So tell me what are all those uh subcurates I should write content about so that I have a higher chance to like rank higher to make sure I cover all the search intent for user because nowadays we all know that is no longer just matching keywords right is more about matching the potential topics a user ask about in that subject. So you want to cover that as much as possible so that you can cover um the whole user journey and not just matching keywords. And this is exactly Noble LM can help you do and I always think Notebook LM is a secret SEO tool because it will give you a lot of insight how you can plan the content by just import know what is working in the eyes of Google. So, >> wow, Grace, this has been really interesting and hopefully people have a lot of new perspectives on how they can use different tools to accomplish different things as part of a workflow or process that they need to do for their work. >> Now, Chris, if people want to connect with you on the socials, where do you want to send them? >> And if they maybe want to work with you, uh, where should they go? >> Um, so they can follow me on my YouTube channel. I share um um AI and about digital marketing and if they want to work with me um they can submit a form there's a contact form on my website gracelearn.com or if they also just you can yeah they can also just find me on LinkedIn Instagram send me a DM that might be even faster um so that's how people can find me and also on X yeah >> awesome and folks uh Grace spells her last name L EU NG Grace, thank you so much for coming on the show and sharing your insights with us today. >> Thank you so much for having me today. That was a great shot. Thanks. Thanks, Michael.