Gemini 3 Deepthink For Beginners - Gemini 3 Deepthink Full Guide With Usecases

Google Gemini Deep Think is here. Let's dive into the tutorial. So, in order to activate Google Gemini Deepthink, it is just here on the tools tab and you type in deep think. Now, do remember that deep think is only available for the ultra subscribers and that is because at the moment, this is a model that essentially spins up multiple different sub aents and then does a bunch of reasoning in a parallel. So, this is a model that is particularly powerful for multi-step reasoning problems. So, it's important to know that Gemini 3 Deep Think is essentially worth trying if you rely regularly on AI for serious research, coding, or strategic planning where reasoning matters more than the speed and the subscription cost. And before I dive into this tutorial, please do remember that deep think is essentially a model that will take around 10 to 20 minutes per query. So, this isn't something you want to use if you're trying to get a response super quickly. So, in the Google demo video, one of the things they showed us that Google Deep Think is actually good at and something that I found was really cool is that you can basically take any sketch and then convert that to a STL 3D model. So, I actually used Nano Banana to generate this image of a sketch of a headphone stand. Don't think about the design here. This is purely just a sketch. And so what I actually did for the prompt was I said a sketch of a headphone stand, no headphone on it, and a single object. The reason I've done this is because in that short demo, they did show us that we could make these things. And in the sketch that they used, it was a singular image with nothing else in there. Make sure you're using this prompt because if you don't, essentially, Natabano will generate you an image that just has a bunch of different artistic things on it and it isn't really useful for the model. So you want a clear image that is simply a simple sketch. And so once you have this, okay, and you've got your prompt in, you can essentially say here, and this is the prompt from my community, transform the attached sketch into a functional 3D object. Generate a single file HTML using 3. js that builds the geometry based on the sketch. The geometry must be a manifold watertype mesh suitable for 3D printing. Implement orbit controls for 360° inspection. Include a download print ready STL button using the 3. STL exporter. and use a minimalist dark mode UI overlay yada yada and then essentially what happens is once you get this field and you click copy and then if you paste this into note and then you save this as a HTML if I decide to now open that HTML you can see that the you know object is right here and this is what we got to see from the demo from Google's actual demo video and yeah I think this does look really cool and arguably the biggest thing is not the 3D object itself of course being able to have an STL file that you can literally just download. And I think it's super useful that the fact you have the total height, the total solid volume, and the estimated weight. So all of these things are super useful for people who are wanting to 3D print things. Of course, there are different ways that you can achieve this. And of course, you can debate on the complete accuracy of the model, but I think something like this is super useful. And so yeah, that is one of the basic use cases, but let's actually dive into some other use cases. So another thing is standard research deep think is going to think longer and harder about this problem. So if you have something that is essentially a issue where you want to think about the problem a lot longer and you really just need a lot more you know insight this is I guess you could say thinking about the problem itself which is where this is a little bit different to deep research. Deep research tools are basically focused on gathering large amounts of information. But deep think is essentially focusing on the actual problem itself and reasoning about that conclusion. So I just want to make that clear because it is a little bit of the same product but with slightly different outcomes. They're just reasoning about the problem to a greater extent. So this agentic research prompt here is where I've said I've I'm writing a long form piece making YouTube video on why AI isn't a bubble. Map out every argument, counterargument, and piece of evidence I'd need. flag anywhere my reasoning or my logical gaps or where I need stronger sourcing. And um this gives me a really good prompt. It gives me act one, the crucial bubble, you know, it talks about the different stances and you know why they're different and then you know the core arguments then the shift and yada yada. And it actually does come with sources as well. So if you want to do some kind of research in here, it does come with sources as well. Now, notably, it doesn't, you know, go through as many sources as deep research will, but deep think is just something that where, you know, if you want to go through this kind of problem, maybe you've got a research problem that includes a problem that you really need to think about, this is going to be a use case as well. So, for me, for some super obscure or weird or strange topics, this is where I would probably use deep think because it's still going to be a useful tool. and that probably I would only use it after the deep research probably just didn't give me something I want to use. Now, this is another example of where you'd want to actually use the model's reasoning capabilities in terms of yes

you want to have some research, but you wanted to think harder about the problem. And this is essentially stock research. Now, I'm not a financial adviser. I can't give you financial advice. But I do think that since the model is going to think longer and harder about the problem, you may be able to see different insights about a specific stock that you didn't know before. So I use this prompt once again. This one's in my community. Walk me through a deep fundamental analysis of Adobe. Consider the macronomic headwinds, sector trends, balance health sheet, and competitive mode. Where is the consensus wrong? And so when you have deep think, this is why I would use this model for this kind of research because the model is going to think harder about all of the different problems. Whereas with deep research, it's going to think about the problems, but it's not second order, third order consequences. It's mainly just going to think about getting you that information. So you can see here, it actually gives us the bare case. It gives us the, you know, balance sheet information. It gives us the competitive mode, and bottom line. a factor that says the entirety of the world is missing the fact that Adobe is quietly monetizing the entire back end of the AI revolution cemented by the Seamrush acquisition. So these are going to be, you know, things that other individuals may miss. I know that not everyone buys stocks or invests in stocks, but the point is that even if you aren't researching stocks, if you're researching a company and maybe you're confused about what that company does or how they fit into the economy or even their business tactics. So, I guess you could call this, you know, kind of business research, this is going to be that kind of thing that does help you because it thinks about the actual problem longer than it would think about, okay, I've got the research. Here's the conclusion that I've instantly made. Now, remember how I said deep think is a model that reasons for much longer and harder about the problem. One thing that it is really good at is when you have different data sources and you're essentially able to use that to come to new conclusions. So, I've got this prompt which is basically YouTube data analysis prompt. And quick tip for your prompting guys, don't overdo it with the prompting. The model already has an inbuilt chain of thought reasoning mode. So, you don't need to say think like this or think like that. it is going to completely go ahead and think long term. So please understand that it's going to really reason ahead like it's already designed to reason a lot. So I've said analyze my YouTube channel data thoroughly. Find non-obvious patterns, correlations, and actual insights I'm probably missing. Look at the upload times and days. What's the best performance? And basically I just said after all of these things, give me the five biggest changes I should make based on data. And so it reasoned for around 15 to 20 minutes. And this is the second channel by the way. And it gives me some really insightful data. So it gives me the length fs retention paradox. It talks about some paradox on my channel. It says the growth declines on certain days and it gives me the best days I should be uploading and it gives me the hidden traffic outliers and top five actionable changes. So the reason this kind of thing is useful is because is going to think longer and harder about any problem than you would. Okay? And considering that if you're working maybe on a business, maybe you've got a project at school, maybe you've got your health data, it's going to be able to reason about that data set in a way that you might not have seen. So, we all know that analyzing data and spotting trends is super useful because that's how we can predict the future, make changes, and adjust our path. And so if you have any online business, maybe you've got some messy data, maybe even your finances, this is going to be something that is super useful where you want to get real actionable steps that are completely based on the actual data. So it's not just, hey ch, I'm feeling down. What should I do? This is pure quantitative data where you can take that stuff, analyze it, and it's going to reason for a long time about it. So it gives me some clear pivots. upload days. It gives me some clear video length. And then it says capitalize on this searching trend. So stuff like this is extraordinarily useful. And like I said, traditional models may not go this far because they're not trained to reason as long. Now, of course, since this model is good at coding, I actually asked it to build a Sims style 3D room builder. I actually found a similar prompt on Twitter and just tweaked it a bit. And so Deepthink can essentially create things like this. Now, of course, I will say that these things are pretty useful, but useful in the sense that they're like demos and stuff. So, of course, you can't really build full scale applications, but things like this are still pretty useful in terms of your ability to mess around and develop maybe early prototypes for things that you want to test. So, I think deep think is just something that has a level of coding ability that is incredible because it's meant to oneshot things. And so I'm not sure about you guys. I don't usually code that much. It's not really a part of my everyday workflow. But sometimes I do like to code up small things where I can not only visualize data, but I can make small experiments to test things before I have to. And that is where this is something that can be used for. Now, I'm not sure what you want to use this for, but of course, like I said, there are a million different things that individuals could use this for. Maybe like room planning

or something like that. I don't know. I'm not that creative. I don't have the best ideas right now. All I'm trying to do is showcase you guys the very best use cases you can. And if you want to oneshot something in around 10 to 15 minutes and you can leave deep think for reasoning on the problem, that is going to be something that it can do. Of course, if you enjoyed this video and you want to get the prompts, don't forget to join the community or if you want the cheat sheet, which is where you simply just download this image and the prompts in the community. It's all going to be in there. It's always updated every single day because I make these videos every single day. Otherwise, let me know what other videos you want to see.