Give Me 9 Minutes, I'll Make You AI-Native

Working with AI comes in roughly three levels. First, we have people who are AI curious. This group relies on the free tier of AI tools and only uses chatbots when someone reminds them to or when they're stuck. Level two, we have the AI literate. These people pay for AI, maintain a prompts database, and they know when to use which AI feature and model. Level three is AI native. And in a nutshell, these people have redesigned their workflows assuming an AI collaborator exists. Most professionals are stuck at level two. So, in this video, I'll share the specific strategies ordered from simple to advanced that will get you to level three. Let's get started. Kicking things

off with the easiest habit to adopt, leave AI breadcrumbs. What this means is instead of treating AI chats as disposable one-off threads that become almost impossible to find again, you want to create a hyperlink to the conversation and paste it directly into the document where you're actually using the output. I know this habit sounds silly and insignificant, but it works great thanks to the core productivity principle of always organizing your information by where you will use it, not where you found it. Diving into a real world example, here's the actual Google Doc I used to prepare for a recent work presentation. The final outline tab has all my content and the helpful hints tab has hyperlinks to my AI conversations. Taking a step back, let's say I'm building this presentation from scratch. I would first ask AI to rewrite my initial rough prompt so that it's optimized for the model that I'm using. And once I press enter, you'll notice the URL transforms into a unique link. Right? And this is where I'd press command or control L to select the entire URL, copy, come back to the Google doc, type out Gemini, command or control K to hyperlink, and paste that link. I then copy the optimize prompt and paste that into a new chat. Make adjustments as needed. then go back and forth with the AI to brainstorm and refine my presentation outline. And of course, I save this new chat link in the Google doc as well, so I can easily pick up where I left off a day or even a week later. Pro tip, add context next to each hyperlink so you remember why it matters. For example, here, this Gemini conversation was helping me brainstorm my outline. This one was around applying storytelling principles to that final outline. And I use Claw to refine my final talking points. And just to be clear, I like to test multiple frontier models because I do this for a living. Most people should just pick one AI chatbot and get really good at it. Here's another real world example. Whenever I create a new project page in Notion here under the op center section, I would add links to the corresponding CHP and claude projects so I can jump into those AI workspaces immediately. Put simply, leaving AI breadcrumbs means organizing your AI chats by work context and not by date or chronology. This beats trying to search for that one specific thread from days or weeks ago. And the rule of thumb here is simple. If the AI conversation took more than 10 minutes or produce something you'll reference again, anchor it to your workspace immediately. Speaking of systems, I'm actually building an entire course on evergreen AI skills that teaches universal principles for any platform. So, if you want a framework that never goes obsolete, click the link below to join the weight list. Moving on to habit

number two that requires a bit more effort, build an AI swipe file system. In a nutshell, instead of prompting AI with basic instructions like write a business proposal, you provide a specific example from your curated library aka your swipe file and ask the AI to first analyze what makes it so effective. Then apply those patterns to your new content. For instance, let's say you work in OpenAI and you have this brilliant idea that you know users around the world will absolutely love pumping chat full of ads. Instead of starting a proposal from scratch, you open up your AI swipe folder to find examples of business proposals you've previously saved. Share them with the AI and say, "Analyze the business proposals I've attached, list the key patterns in structure and tone, then apply those patterns to my content below. " And you paste your advertising product idea designed to maximize shareholder value. Jokes aside, I guarantee you that initial output will be stronger than any initial draft you could have come up with yourself, not to mention the massive time savings. Funny story, when I was at Google, I used this technique for all my important presentations by uploading slide decks from Mackenzie Bane and BCG. And senior leaders who came from those firms would ask if I had also worked there before. And I'd be like, "What? No, these frameworks and principles come so naturally to me. Does it not come naturally to you? " And that's why I'm not at Google anymore. No, just kidding. But as you can see, this technique is so effective because it gives the AI a clear picture of what good looks like, allowing Chachi or Gemini to produce output that matches those standards instead of generic slot. To close the loop, the actual habit you want to develop is whenever you encounter excellent work in your field, immediately save it to your swipe file system so you can reference it the next time you face a similar task. Pro tip: start narrow and expand gradually. Begin with just two to three use cases you do repeatedly like presentations, emails, or reports. And organize your folders by use case, not by source or date. By the way, this is also the first step in making your Google Drive AI ready, which is something I dive deeper into in the

course I just mentioned. And number three, we have AI first task planning. Heads up, this habit is probably the hardest to maintain consistently, but I [snorts] promise you, just like going to the gym, it will make a massive impact over the long run. Put simply, this habit involves planning your AI use before you start a big piece of work. This means breaking down complex projects into small concrete tasks, then marking the ones AI can and should help with. Diving right into real world example, I used to be responsible for sending these uh weekly newsletters to our Google Ads customers uh with the goal of driving adoption of new product features, aka getting them to spend more money. Basically, before writing anything, I'd break down the work into steps and microtasks. Then I decide whether to do each microtask manually or use AI. If I use AI, I specify the exact tool that's best suited for that task. In this case, there are three main steps. Step one is to clarify the goal and audience. Step two is to draft the newsletter. And step three is to refine the copy for Google's brand voice. Now, step 1. 1 would be to brain dump key information like what the new feature is, the benefits, who should use it, and so on. This task is manual because there are details I know that AI doesn't have access to, and I also want to inject my point of view. Step 1. 2 is to fact check my notes from the previous step. Here, it makes sense to use AI, specifically Notebook LM, since it has the lowest hallucination rates. And so I upload my brain dump and source documents onto a notebook to verify rollout dates, feature names, policy details, etc. Step 1. 3 is to turn those fact check notes into a structured brief. This is obviously also a perfect task for AI, but this time I use the standalone Gemini app instead of notebook LM because the Gemini app is much better at creative writing. In the interest of time, I'm going to skip over steps two and three, but the process is exactly the same. List out all the microtasks. decide if AI should help and if yes, pick the right tool for that specific job. At this point, the benefits should be pretty clear. First, you cut decision fatigue and context switching because AI usage is already preddecided at the task level. Second, you increase quality and speed by matching the right AI tool to the right kind of work instead of forcing one tool to do everything. And the rule of thumb here is for any project that will take more than an hour, spend 5 to 10 minutes mapping the steps and tagging which ones are AI or manual. For the productivity nerds out there, this is a classic example of sharpening the axe, where spending a few minutes on planning up front saves hours of work later. Pro tip, create templates for recording workflows, like what I have here for my weekly newsletter, so that next time you can focus on executing instead of having to plan from scratch. This brings us to a

bonus habit that ties everything together. Maintain a prompts database. I've talked about this many times before. Whenever you write a prompt that works well, save it to a central library organized by use case so you can reuse that prompt whenever you face that task again. The worst feeling is writing a perfect prompt 3 weeks ago that generated a perfect output. But today you can't find it. So you try to rewrite it from memory and the result is just eh, which is also my girlfriend's reaction shortly after we started dating. Huh. A lot of you have been asking for my go-to prompts. So, I spent quite a bit of time putting together a set of essential prompts that everyone can benefit from because I genuinely believe you don't need a thousand random prompts. You need 10 to 15 battle tested ones that you can use every day. I've been using and refining these ever since Chachi PT first launched. So, if you want to skip the trial and error, I'll leave a link to this down below. See you on the next video. In the meantime, have a great one.