So if you're using AI just to write slightly better emails, or maybe summarize some text, you might be missing out on, well, a lot. That kind of basic stuff is fine, sure, but it really... barely scratches the surface of what's possible when you get into more complex work. That's really true. You know, when you look at any real project like building software, planning a marketing campaign, even something physical like a carpenter building a table, it all follows this fundamental
pattern. Input, then processing, then output IPO. And our goal really is just to maximize the value of that final output. Systematically using AI helps us do that. Welcome to the Deep Dive. Today our mission is to help you turn AI from maybe a random chat partner into a real systematic partner for design for strategy. We're pulling the best ideas from the source material we looked at on how to build these powerful workflows. Yeah, we've got a pretty clear path laid out.
First, we're going to properly define that IPO structure, input, processing, output, what it means. Second, we'll talk about the essential tools you kind of need and some setup tips you can't skip, especially around AI memory. That's key. And finally, we'll walk through two really specific examples using AI and product design. and then for creating social media content at scale. Okay, let's unpack this IPO model first then. The simple brilliance here is that AI can
actually help at all three stages. It's not just something you plug in at the end to maybe clean up your writing. It can speed things up everywhere. Exactly. So start with input. This is where AI tackles, well, the messiness of real life. Tools like VoicePal or Grain were mentioned. They're important because they take messy, stuff -spoken ideas, rambling voice notes, maybe an hour -long meeting, and turn it into organized text the
AI can actually understand and work with. They handle that transcription and maybe some initial source. Right. So that cleaned up data then moves into processing. This is like the central hub, the brain of the operation. You need a strong foundation here. That's why something like Claude was recommended. What's important about Claude or similar advanced models is that big context window. Yeah. And that context window is absolutely essential. It holds the memory for a complex
project. It does the heavy lifting. organizing, checking data fast, planning things out, even drafting, all while keeping the project's history sort of in mind. And then get the output. This is where the finished thing comes out. Polished. We saw some neat specialized examples. Firecut for video edits or Gamma turning text drafts into presentation slides pretty quickly. So the toolkit kind of mirrors the structure. Right.
Input tools for cleaning up, central processing brain, and then output tools for getting it done fast. OK. So thinking about that central processing hub, the one holding all the project memory. Yeah. What would you say is the absolute most crucial first step before you even start a complex project? Ah. You have to teach the AI who it is and what the mission is. Precisely, yeah. If you want the AI to act like a professional partner, you absolutely have to personalize it.
The most critical setup step is making sure that memory is solid. This really means avoiding having to explain your job or the company voice or the whole project background every single time you log in. That step is so vital because otherwise the AI just defaults to generic textbook answers. Right, right. And speaking of context, another key setup piece is file uploads. You need to feed it your own data, PDFs, spreadsheets, style guides, so it can check things against your actual
real -world situation. your constraints. Mm -hmm. You're basically setting up a clear data structure, not just randomly dumping files in. You're telling the AI, this is the hierarchy of knowledge for this specific task. You know, I still... Well, actually, I still wrestle with prompt drift myself sometimes. I remember trying to map out a new hiring plan in a chat where I'd just been drafting like... casual messages to friends. And suddenly the AI started talking about aligning core values,
but using like really aggressive slang. Total meat of the tongue. Got completely confused. Oh, yeah, that's a classic problem. And that's exactly why the source material really emphasized the project's feature tip. You've got to create separate, dedicated spaces, like one called Q4 marketing strategy, another personal blog ideas. It keeps the information quarantined, stops the AI from getting rixed up, keeps things focused. So when we use these dedicated projects, these
separate spaces. Yeah. What's the key confusion we're stopping the AI from making? It stops the AI from blending your different job contexts together, keeps work separate. Okay, this is where it starts to get really practical. Let's test this theory. Workflow one, using AI as a product manager. Let's imagine we're designing a simple budget app for students. We'll call it student money. Right. So step one needs really clear instructions in the prompt. Define the
user, the persona. You tell the AI to act like an expert user researcher. And you ask for specifics, deep specifics, target audience. Vietnamese university students may be 18 to 22 years old. And crucially, you define their main pain point. Maybe they feel lazy and just avoid writing down every single expense. That emotional detail matters. Yeah, if you don't give it that level of emotional context about the user, the AI will probably just give you a generic feature list, not a real
solution. Exactly. Then step two is designing the user flow. Based on that lazy persona, let's call him Anne, you prompt the AI to design the absolute easiest flow possible. Goal, add an expense in under five seconds. So maybe the AI guesses the category. If Anne types fuk long, does it just assume coffee? OK, but hang on. If we make it that simple, just relying on guesswork,
don't we lose accuracy? How do we make sure Anne isn't just tagging everything as coffee and maybe missing big things, like rent or a tuition payment? That's the exact question you posed to the AI. You ask it to analyze that specific trade -off. You set the hard constraint under five seconds, and the AI has to suggest the compromise, how to balance speed and accuracy. See, it shifts your job from designing every little screen yourself to basically challenging the AI's design assumptions.
Ah, OK. That makes a lot of sense. So we've defined the user, the flow, debated the trade -offs. Mid -roll sponsor, read, placeholder. Welcome back to the Deep Dive. We've got our user Anne and the quick expense adding flow defined for our student money app. What's step three? Ah,
right. Ask for a simple model. A prototype. So we instruct the AI to create a clickable prototype, but we specify exactly what screens we need, and the key elements, like home screen needs a huge O plus button, input screen, only show the number keyboard, keep it minimal, and then maybe an automatic done screen after input. Yeah, this is where the AI does that translation, takes those abstract rules you set and turns them into something tangible, something visual you can
react to. You're not painstakingly drawing boxes and arrows, you're giving the system the rule to design for you. And then we hit step four, test and fix. iteration, we challenge the AI's prototype, throw an edge case at it, like, how does this super simple app handle a loan Anne gave to a friend? We make the AI analyze the trade -off again. Do we add complexity, like a loan section, or does that break the core simplicity needed for Anne? And the big lesson here, really,
is you don't need to be a coder. You don't even need to be great at wireframing tools. You just need to be incredibly clear about describing the requirement, the problem you're trying to solve for the user. The AI handles the translation into a testable model. So what core function is the AI really performing in this whole design loop then? It quickly translates abstract ideas into a tangible model for review. Okay, let's switch gears. Workflow 2. AI as a content strategist.
Thinking about fast platforms like TikTok or Reels, often the hardest bit isn't making the video. It's coming up with ideas that actually connect, that resonate. Step one here is about getting emotionally effective hook ideas. We don't just want generic topics. The prompt needs to tell the AI acting now as a social media marketing strategist to generate maybe 20 ideas and then crucially sort them by psychological triggers that would work for our user and things like
pain point, curiosity, quick win. I really like that focus. It forces the ideas to be relevant right from the start. Something like why you're always broke by the 20th and how to fix it in 30 seconds. hits a pain point, offers a quick win. Exactly. Then comes step two, which is critical. The human filter. You have to apply your feeling. The AI gives you 20 psychologically sorted ideas, yeah. But you only pick the best, maybe three to five that genuinely resonate with you, that
feel right for your voice. You're the curator here. Wow. Just imagine scaling that. Testing thousands of those kinds of psychologically angled concepts in like minutes, that speed for strategic planning, it's kind of mind blowing compared to traditional brainstorming. It really is a total acceleration of that early creative phase. Okay, so now step three, creating a detailed
script. Right. You take one of your chosen hooks, let's use this while you're always broke by the 20th, and you prompt the AI for a full, say, 30 second script. The key here is using a structured format, like a table, time column, words column, visual column, and you need specific visual directions in there, like zero three seconds, close up shot of your face looking serious, or 10, 15 seconds, fast cuts showing three small, impulsive buys
coffee snacks. Which leads perfectly into step four, and this is maybe the most important point, adaptation. Do not just copy and paste that script. Please don't. Read it. Understand the structure, the flow, but then you absolutely have to adapt it. Use your own voice, your tone, your humor. The final video, the final content, it must feel like it came from you, a real person. Beyond just making it relatable, why is keeping your own specific voice so crucial in that final step?
What risk are we avoiding? Authenticity ensures the content feels human. Avoid sounding generic or predictable like an algorithm wrote it. So just to quickly recap the big ideas for you listening, AI really shines when you integrate it properly into that input processing output system. That
random one -off use, it needs to evolve. Yeah, and critically, AI shifts from just being a tool to being a real partner, like a product manager or a content strategist, but only when you give it super detailed role -specific instructions. You got to treat it like a highly skilled con... and Sultan you've hired, be specific. And these workflows we talked about, they're really just the beginning. The source material hinted at
more advanced stuff. Things like using AI to understand feedback from thousands of customers automatically, or even building out a whole online course structure from just a basic outline. Pretty powerful extensions. So here's a final thought to chew on. If you get really good at defining the problem perfectly, and defining the user persona with deep clarity, and setting those strict rules for the AI to execute the plan,
Are you still the creator in the old sense? Or have you become something else, maybe a highly effective curator of intelligence? Definitely something to think about. We really encourage you to stop using AI just randomly. Start thinking about and maybe building your own IPO systems today. Thanks so much for joining this deep dive. Until next time.