Most people use AI like a fancy search box. We type a simple question, get an OK answer, and we move on. It's pretty transactional. But what separates that casual user from someone who's operating in the top 1 %? It's not just asking better questions. It's teaching the AI how to think and how to build for you. And that's really the core difference, isn't it? The AI is built for speed, maybe not always for perfection. So
welcome to the Deep Dive. Today yeah, we're talking about transforming chat GPT moving it from just a quick answer machine into well a systematic personalized thinking partner We've looked at a whole stack of power user guides really dug in and we're gonna distill it down to three core shifts you could make first is making the AI self -correct second giving it a kind of long -term memory and Finally training it to be your specialist coach right our mission here is pretty
simple We want to move you from just asking for those quick answers to actually building customized Autonomous systems systems that can save you out every single week. Okay, so let's unpack that first shift. Systemizing the output. We kind of know the first answer Chet GPT gives. Well, it's often just average, right? It's the fastest thing it can generate. And sometimes speed isn't what you need. It can be the enemy
of quality. Exactly. It defaults to speed. So the first big strategy here is something called the self -check method. And this basically forces the AI to do better work by building critique. right into the prompt itself. Yeah, it's a prompt trick, basically. You don't need anything fancy. You're telling the AI how to think before you tell it what to write. It's like we build a little internal review process, but it's hidden from you, the user. OK, so how does that work in practice?
How do you build that internal critique into just one prompt? What are the steps? You set up a four -step sequence. So step one, think inside. It outlines what makes a good response. But, and this is key, it doesn't show you this thinking. Step two is write draft one. which also stays hidden. That's usually the fast kind of average answer. And the magic happens in step three, right? Check and fix. This is where you tell it, OK, critique draft one against those
rules you thought about. Find the weak spots and fix them. Exactly. And then finally, the AI only shows you the superior draft two. And this works because it forces the AI to kind of slow down, dedicate resources to self -reflection. It catches its own mistakes before you do. So the final output is just dramatically better. It's about demanding that internal accountability. That self -correction makes the output cleaner, for sure. But the builder mindset also means
connecting the AI outside, right? How do we take that better text output and turn it into finished products? Ah, yeah. This is where tool integration comes in. We're moving beyond just text. You can connect third -party apps now, things like Canva or Figma, right there in the ChatGPT settings. OK, so think about presentations. Let's say you upload a five -page document, maybe a quarterly
marketing report. Pretty dense. You could tell ChatGPT, I want a 10 -slide draft, maybe five slides summarizing findings, three on key takeaways, and I don't know, two on next steps. Right, so chat GPT reads the content, figures out the summaries, and then tells the connected Canva tool, hey, generate some professional designs for this. You instantly get like a 90 % finished presentation. That's a huge time saver. You're not starting from scratch. You're basically editing something
that's almost done. Or take Figma. If you need a workflow diagram, maybe for onboarding a new employee, which can get complicated, you just give it simple text instructions, define your colors, say blue boxes for actions, orange diamonds for decisions, and boom, it generates a perfect, shareable flowchart. The real power here is that it removes the technical skill barrier. You don't need to be a Canva wizard or know how to manually draw stuff in Figma. You just describe the result
you want, and the AI builds it. So if the core difference is forcing that self -reflection and integration, what's the single biggest takeaway for everyday prompt writing? Always instruct the AI how to think before you tell it what to write. Okay, the next big shift then. This involves giving the AI a persistent brand persona. Basically getting rid of that repetitive work of explaining who you are and what your style is every single
time you start a new chat. Yeah, that constant explaining leads to what we call prompt fatigue. It's tiring. And that's exactly why the custom instructions feature is, well, it's critical. It acts like ChatGPT's long -term memory about you and what you like. We saw this great example using a small local business. Let's call it...
Happy Bakery. You set up the instructions to define the brand as warm, friendly, and you enforce specific rules like use short, simple sentences, maybe use certain emojis like a cupcake carry, and always ask customers to visit the actual bakery. And now every single new chat, whether you ask for an email, an Instagram post, whatever, it automatically adopts that voice. Those rules,
the systemic benefit is just huge. You save so much mental energy because you're not constantly reminding it, hey, be friendly or don't use complicated words. But sometimes you don't actually know your own brand voice that clearly right. You just kind of write what feels right at the time. Which leads to this really interesting strategy. Let the AI figure out your brand guide for you first. Exactly. You upload, say, five to ten pieces of your own content that you really like.
Maybe blog posts, newsletters, emails you've written. Then you ask ChatGPT to analyze them. Act like a brand consultant for me. And it creates this detailed brand voice guide. It looks at your existing tone. Is it motivating? Gentle, your sentence style short and punchy, long and detailed, even your brand personality like. Do you sound more like a tough coach or a helpful friend? You know, I still wrestle with prompt drift myself sometimes. Honestly, I'll be like
15 minutes into writing something with it. And I realized the tone has gone completely off track, maybe too corporate or something. And then I have to stop. backtrack, reset the context. Having the AI define my preferred style upfront saves me from that constant resetting. It's a major time drain otherwise. And once you've got that memory institutionalized, you can actually organize multiple specific personalities. The sources talk about using the memory feature to save these
personas linked to a trigger word. Yeah, this is pretty cool. You could create, say, the tough editor persona, train it to be super direct, really strict, just cut out every unnecessary word, and you trigger it just by typing edit now. Or maybe you need a brainstorm buddy sometimes, right? Triggered by crazy ideas. It just instantly shifts its whole role and tone based on that one word. It's powerful. So by institutionalizing
our style, these personalities... Are we maybe risking creative stagnation or is it just about increasing speed? Institutionalizing style frees up your mental energy for the higher level creative thinking. All right, let's shift gears a bit. Let's talk about complexity. The next set of strategies really focuses on using AI to find patterns, whether that's in like raw data or generating creative stuff without needing technical skills. Yeah, let's start with data storytelling.
The problem is pretty common. You get these big Excel files full of numbers, right? sales figures, whatever. And it's hard to see the trends hidden in all those rows and columns. So the solution is you upload the raw file like a .xlsx or .csv - and you ask the AI to find the story in the numbers. You give it a specific prompt. Tell me the best and worst months. What's the general trend? Summarize the key takeaways. Maybe four bullet points. And crucially, ask for one suggested
action based on that data. It turns confusing numbers, like sales reports or even big bank statements, into simple, actionable insights you can use right away. It's kind of like having an analyst on call instantly finding patterns your own eyes might just skim over. Okay, now shifting to creation. This is where things get really interesting, I think. Professional video. We know AI video tools are coming. They're getting better fast. But the bottleneck is often the
prompt quality. So Chet, GPT's role here is writing that perfect, super detailed cinematic prompt. Right. Take a simple idea like a happy dog running on a beach. This is not going to give you a great video, it's too basic. So you ask ChatGPT to rewrite it cinematically. Force it to add details. Instruct it. What breed of dog? What specific time of day? What camera angle should we use? What precise feeling should the scene evoke?
And the result is a prompt that specifies, like, a golden retriever joyfully running along the wet sand precisely at sunset, camera flying low beside the dog, capturing the glint of the golden light on its fur. The overall feeling must be warmth and freedom. That's a prompt that an AI video tool can actually work with. Whoa, yeah, imagine scaling that. A million unique high -quality video scenes generated every single day, all
from just text instructions. Yeah. I mean, that really is the future of digital content, isn't it? And finally, for maybe folks with websites that feel a bit static or boring, our sources point into building simple website widgets using ChatGPT. You don't need to hire a programmer for everything. You can ask ChatGPT to write the full code, HTML, CSS, JavaScript, for something like a simple interest calculator. or maybe a calorie counter. And it generates the code. Usually
pretty well. You can just copy that code block and paste it directly into your website builder like WordPress or Wix or whatever you use. It adds useful interactive features to your site in minutes. That's a massive return on your time investment. So which do you think is the bigger time saver overall? using AI to analyze existing data or using it to generate totally new creative assets. Analysis reveals where you need to act. Creation implements how to act quickly. Okay,
our final core shift. This one feels maybe the most personal. It's about transforming the AI into more of a thinking partner, even a decision maker alongside you. Yeah, and this can start with active learning. Say you upload a complex document, maybe a report on Vietnam's economic history. Don't just ask for a summary. That's passive. Instead, ask ChatGPT, act like a demanding
teacher, tell it. Quiz me one question at a time, hold me accountable, explain why my answers are right or wrong, and keep going until you hit a certain score. That's active learning. It really helps with retaining the information much more than just reading a summary. Okay, next up, organizing big decisions. And when you're planning something complex, maybe comparing Joe A versus Joe B, or deciding between Da Nang and Sapa for a vacation, that single chat thread can get really messy,
right? Pros and cons all mixed up. The power move here is using the branch chat feature, which basically just means duplicating the current chat thread. So you explore option A completely separately from option B to distinct, clean conversations. It keeps your thinking organized and stops that feeling of cognitive overload. And we shouldn't overlook practicing soft skills. Using the mobile app's voice mode lets you practice difficult
conversations out loud. Ask it to role play, like be a professional, serious HR manager and ask me five tough interview questions. Yeah, saying the words out loud actually builds muscle memory. You get to practice your phrasing, work through the awkward pauses, fix the stammers in private before you face the real high stakes conversation. It really helps. And all this kind of culminates in the ability to build a custom
GPT advisor. This is like a specialized version of the AI that you train to think and talk like a specific persona or maybe a certain philosophy. Right, like the example of building a stoic advisor GPT. You train it specifically on stoic principles, instructing it to focus on what's controllable, on wisdom, on calm advice. So when you ask it about, say, a conflict at work, it won't just give you standard HR advice. It might say something like, you cannot control your co -workers' actions,
only your reaction. Focus on what is within your sphere of influence. It gives you this entirely different specialized lens to look at your problems. It really becomes a targeted thinking partner. So does building a specialized GPT like that for advice fundamentally change how we relate to AI? Does it move from just a tool to more of a collaborator? Yes, I think it does. It evolves the tool into a specialized, highly targeted thinking partner. So the common thread here...
Across all of these methods, it seems pretty simple, really. Top users don't just use ChatGBT for one -off answers. They build systems. They teach the AI about their brand, their voice, their processes, their goals. It's about persistence. Exactly. The real shift is moving from that sort of transactional input output to a more persistent contextual collaboration. The difference between a regular user and that top 1%, it isn't really about knowing more hidden features. It's about
using the AI as a partner. a partner that holds long -term context and critically self -corrects its own output before you even see it. So maybe start small. Pick just one method that feels like it fits your workflow right now. Maybe setting up a simple custom instruction for your brand voice. That could be a good first step. The more you teach it about what matters to you, the more valuable it's going to become. Now here's something
to think about. If you used this system building approach to automate one really major, painful task in your week, what system would you build first?