How to Learn AI so Fast, it's Almost Unfair

0:00 People think it takes years to become an advanced AI user, but the truth is there's an unconventional method that lets you do it so fast that it actually feels unfair. And in today's video, I'm going to reveal what that method is and how to use it to become an AI master in just 4 weeks. So, if you're not a

0:16 technical person, but you still want to take advantage of this technology without wasting months going down the wrong path, this system will change everything. Now, before we actually get into the practical details, you need to understand why exactly thousands of people fail to learn AI. You might think

0:31 it is because they're lazy or not trying hard enough, but that's actually not true. The real reason they get stuck is because they fall into one of these three traps. And the worst part is that they don't even know they're stuck because they actually feel productive even though they are the complete

0:45 opposite. So, if you don't know what these are, you might fall into them without realizing it until it's too late. The first common mistake is chasing all the new AI tools and updates. Every week, there's a new model making the headlines and promising it'll change everything. And people actually

0:58 believe it, so they switch tools, start watching tutorials, and learning new interfaces. And because this cycle never ends, most of them end up being mediocre at every AI tool out there. The second mistake I see a lot of people making is actually going too deep into certain topics, like how the model actually

1:14 works behind the scenes. These are important if you're an engineer trying to build an AI from scratch, but if you're a normal person who wants to take full advantage of the current tools out there, this is a complete waste of time. It might seem productive at first, but it's not very practical. And the same

1:28 happens with the third trap, too, which is prompt engineering. AI needs good prompts to create high-quality results, but people take this [music] too far. They get obsessed with writing the best prompt ever, when in reality, all you need is a simple framework I call CRAFT. But more on that later. All of these

1:43 three common mistakes have one thing in common, they're passive learning. So, you're consuming a lot of information, but you're not using it at all. And science [music] has proven time and time again that this method is the worst you can use according to the learning pyramid. Studies show that passive

1:57 learning, like watching, reading, and listening, has a 10% retention rate [music] after 24 hours, while doing a real task gets you to 75%. That's more than seven times faster than what most people do. But this is only the first principle behind the strategy I'm showing you today. The second one is all

2:13 about choosing the right AI tools. And that's because there are so many of them out there that it's really confusing which one to use. And what almost every beginner ends up doing is using ChatGPT for everything. This will give you mediocre results and slow down the entire process. So, to avoid it, you

2:28 need to know the four big AI categories. And once you do that, everything will actually become very straightforward. The first category is the one you've been using the most, the general reasoning engines. These are AI models like ChatGPT, Claude, or Gemini that help you with general tasks such as

2:43 writing, coding, and summarizing. You can think of them as the brain of the entire operation. So, instead of switching between them whenever a post online says one is better than the other, choose the one you like the most and stick with [music] it. And honestly, it doesn't matter which one you choose,

2:58 as they're all pretty much the same when it comes to performance. [music] The more time you spend on one single tool, the faster you'll generate high-quality results. Now, the second category is where accuracy starts to matter more than creativity. Your reasoning engine is great at many

3:12 things, but it doesn't always get the facts right. It generates what sounds correct [music] based on its training data, which is why it can sound confident and still be wrong. But this is where research engines come into play. Tools like Perplexity, Notebook LM, and Consensus pull from live sources

3:26 and cite everything, [music] so you can see exactly where each piece of information came from. If you need to research a topic accurately or work with your own resources without worrying about mistakes, use a research engine. And because this is one of the most important AI categories of all, I'll

3:41 dive deeper into it in the practical learning strategy. [music] Next are the specialists. These are tools that do one thing at a level a general model simply can't match. If you want to build a full-stack app, you can go back and forth with ChatGPT and try to get all the code right, but that'd take way more

3:56 time than it should because ChatGPT is a generalist. It's good at surface level tasks, but it usually flops at really specific ones. [music] That's why you have tools like Codex that are only trained on coding data. This means it's extremely competent at solving programming problems. But this is not

4:10 just for coding. There are multiple AI specialists in every domain you want. If you want high-quality images and videos, you choose tools like Higgsfield and Midjourney. If you want to generate audio that sounds like real humans, you go to 11 Labs. And the list goes on. You choose a specialist when you need

4:25 professional-grade output in a niche domain. However, there's one more category left that will save you hundreds of hours every year if used right. I'm talking about workflow automators like Zapier, Make, and n8n. These don't generate content at all. They just move data from one place to

4:41 another, which can quickly replace any manual work. They're the infrastructure that turns a collection [music] of separate apps into one connected system. When you find yourself copy-pasting the same thing three times a week, that's a process that should be running automatically. So, instead of looking

4:55 for the one tool that does everything, you just need to choose one AI model for each category. And with this, you now have the two core principles behind learning AI faster than anyone [music] else. So, the 4-week path is pretty straightforward. For each week, choose one tool from one category and use it to

5:10 solve a real problem in your life. But if you're still unsure how to do that, I want to show you exactly how I do it if I had to start learning AI over again. Let's start with week one, which is all about the generalist AI tools. I'm going to use Claude for this, and the first thing you need to learn is how to talk

5:25 to it the right way. Most people open it and type something like, "Help me write a report." or [music] "Give me ideas for my business." and then get disappointed when the output is too generic. And that's because the AI doesn't know who you are or what good actually looks like for you. Vague input gets you vague

5:40 output. But you don't need to become a prompt engineer, either. The framework I use for every prompt is called CRAFT, and it has five parts. Context is the first one, and it tells the AI who you are and what the situation is. It can be something like, "I run a five-person marketing agency, and I'm onboarding a

5:55 new client this week." Then the role, which is who you want the AI to be for this specific task. "You are a business operations specialist who has built onboarding systems for agencies." Next comes the ask component, which tells it what you actually want. "Write a five-step onboarding checklist for a new

6:10 retainer client." Then I'll write, [music] "Structured as a numbered list, one sentence per step." which is the format of the output I want to get. This is very useful because it restricts the AI to a certain format. If you don't do this, you'll probably get a huge block of text that's impossible to read.

6:24 Finally, let's add the tone, professional but warm, like welcoming someone to the team. This framework lets me write a full prompt in less than a minute, and it gets me results that need just a few more touches and are ready to send. But editing these is what makes everything feel human. For the example I

6:39 got from Claude, I don't really like the first line because it feels like AI. So, what I'll do is tell it, "Make the opening line more personal." And on top of that, I also want to target a specific type of person with this. So, I'll type, "Rewrite this for someone who's already watched their content."

6:53 [music] Now, this really sounds like me. Each generation will make you better and faster at using generalist tools. The real secret to getting insanely good is to try to solve a real problem from your life with AI every day. This is what science calls active learning. And as you've seen, it's seven times faster

7:08 than just watching tutorials. Now, for week two, you add the research engine. And the specific reason it comes second is that once you've learned to prompt well, research tools become significantly more powerful because you know how to ask them the right questions. For this, I'll start with

7:22 Perplexity, which will actually go on the internet, look for the resources that contain the information I need, and then cite them. In this way, it guarantees the information I get is real, which is not always the case with models like ChatGPT. Let's say I want to do a full academic research on how sleep

7:37 affects memory and learning. I'll also tell it to get information from papers that were published after 2024 only to get the newest ones. So, let's [music] type this prompt for Perplexity. And as you can see, it generated a comprehensive summary for me, which I can use to present at any time. And if I

7:51 have any doubt about a fact, I can just check out the sources myself. However, the most powerful research engine is by far Notebook LM. This lets you add your own documents as resources, [music] so the AI will only respond based on your information. You can upload anything from PDFs to Google Docs and

8:06 YouTube videos, which makes it perfect for any project. Here are a few documents with transcripts from calls I had with my clients. Now, instead of going through them myself, I can simply paste them in Notebook LM and start asking questions. And because it only draws from what you uploaded, every

8:21 answer traces back to the exact document and the exact line [music] it found it in. But the feature that changes how people actually use this tool is the audio overview. In just one click, you can turn your entire source library into a podcast. Two AI hosts will discuss everything in your documents, so you can

8:36 listen to them while you're commuting or even at the gym. This feature was actually a game-changer for me. And if you want to go deeper on this one specifically and learn how to master it, I made a full breakdown of Notebook LM. I'll leave a link in the description below. Now, for the third week, it's

8:50 time to add a specialist. Now, the AI tool you choose for this depends fully on what type of problem you want to solve. If you're creating visual content, you can choose Midjourney or Higgsfield, which is exactly what I'm going to choose for this example. But if you want to create high-quality audio,

9:04 you can choose 11 Labs or Cursor if you're trying to build a software [music] app. The goal for week three is to fill the gap between what your reasoning engine produces and what a specialist produces. So, now I'll create a realistic image of a girl sleeping, which I could use for the research I

9:19 made with Perplexity. For this, I'll go to Higgsfield in the image section >> [music] >> and paste this prompt. Now that we have this realistic photo, we can quickly turn it into a video just like this. The results are really, really impressive. It kept the character consistent throughout the entire video, and all the

9:34 motion feels natural. This is the type of result only a specialist tool can get. At this point, [music] you'll already be better than 80% of people who are still stuck watching tutorials. But if you want to get in the top 1% of AI users, you need to [music] move faster. For this, you'll learn how to implement

9:49 automation in the final week. Again, what you want to do is to try to solve a problem from real life. In this [music] way, you learn through practice, and your brain will learn way faster. So, think about one thing you've been doing repeatedly in the last 3 weeks [music] and try to automate it. Maybe every time

10:03 you get a client inquiry through a form, you manually copy their details into a spreadsheet and send a confirmation email. What [music] I do for that exact situation is to set it up in Zapier. You open Zapier and click create a Zap. Your trigger is the form, so select Google Forms, choose new response, [music] and

10:18 pick the form you want. Then you add your first action, Google Sheets, create spreadsheet row, and you map each form field to its column. Then a second action, Gmail, send [music] email. You write the confirmation template once, and Zapier automatically fills in the client's name from the form. It takes

10:32 less than 10 minutes to set up, and from that point, every inquiry that comes in adds itself to the sheet and sends the confirmation without you ever touching it. This is exactly how I learned AI so quickly, and honestly, NotebookLM is a big foundation for my entire skill. So, if you want to become a top AI user,

10:48 click the video on the screen where I'll show you how to master NotebookLM. Thanks for watching, and I'll see you in the next one.