If you're using Chhatubt, Claude, Gemini, Perplexity, or other AI tools and you're getting responses that sound confident but might be completely wrong, you're probably thinking, "Well, I just need to be more specific, or maybe I need better AI tools." That's exactly what I thought, too. But after testing thousands of prompts, I discovered something that completely shattered everything I believed about AI. The problem is that most people are speaking a completely different language than what AI actually understands. The skill of bridging that language gap is called prompt engineering. So, in this video, I'll teach you how to speak AI's actual language. Plus, I'll show you advanced hacks that make AI admit when it's guessing, get AI to quality control itself, and give you way more confidence in AI's output. All right, let me show you what's actually going on behind the scenes when you get inconsistent AI results. AI seems unpredictable, but there's actually a method to the madness. Every response follows exact mathematical patterns based on what's called token probability. So when you type write me an email, the AI calculates the probability of what should come next. But most people are giving it the wrong type of data. You're giving it random words when AI needs structured probability inputs. Without the right type of data, the AI is spinning a roulette wheel between millions of different email patterns. Business email, personal email, sales email, breakup email. It's literally guessing. But once you understand how to feed it structured data inputs instead of random details, everything changes. You guide it to the exact pattern that you want instead of letting it guess. And that's exactly what I'm about to show you. Now that you understand how AI actually works, and before I show you the crazy hacks that are going to blow your mind, you need to understand the foundation that makes everything else work. The gold standard for prompting and what gets consistently amazing results is using a six-part framework. This aligns with what Google teaches in their 9-hour prompt engineering course that I completed. This isn't a secret. It's just that most people never learn the structure. Let me walk you through this using a quick example. Let's say you have a fitness app and you're crafting an email to reach out to your customers. Number one is the role. Tell the AI who it is. This sets the foundation for how AI thinks and responds. So, you tell the AI who it should be. Instead of getting generic robotic answers, this gives you a specific voice and expertise. So in this example, we'd start with you are a fitness app founder. Number two is the context. So set the situation and give the AI some background. So for this example, we could say reaching out to users who downloaded the app 2 weeks ago but haven't logged a single workout yet. So just explaining the background. Number three is the task. So being specific about what we actually wanted to do. So, for this example, we could say, "Write an encouraging email that motivates them to try their first 10-minute workout without making them feel guilty." Number four is the format. So, we need to define the output. So, do we want it in bullet point form? Do we want a certain amount of word count? We need to specify how we want the information. So for this example, we could say something like return as a 100word email with an upbeat subject line, empathetic opening, one specific action step, and motivational closing tells exactly what we're looking for. Number five is rules. So set some boundaries or constraints. Is there anything you don't want it to talk about? Are there things that you do want it to say specifically? This is the opportunity to do that. So for this example, we could say something like, do keep it supportive and encouraging. Don't use guilt, shame, or pressure tactics. Tells it exactly what to avoid. And number six is examples. This is the secret sauce and where most people miss the real power. You can show the AI what good looks like and you can do it in a couple of different ways. Depending on what you're actually looking for AI to help you with, you can give it text examples or you can upload actual images, emails, documents for the AI to analyze and match. So, for our fitness app example, I could upload a motivational email and say something like this. Match this energetic and supportive tone that makes people feel capable and referred to the uploaded image. Now, let me show you the transformation that this makes in real time. Here's what a lot of people would type. Write me a fitness marketing email for my fitness app. And here's what we get. Generic, templated, could be for any business. Now, let's put all six parts together of the example that we just went through just to compare. You are a fitness app founder, reaching out to users who downloaded your app two weeks ago but haven't logged a single workout. Write an encouraging email that motivates them to try their 10-minute workout without making them feel guilty. Return as a 100word email with an upbeat subject line, empathetic opening, one specific action step, motivational closing. Do keep it supportive and encouraging. Don't use guilt, shame, or pressure tactics. And match this energetic, supportive tone that makes people feel capable. And refer to the uploaded image. Look at that transformation. It sounds like it's written by a real person and speaks to the exact issues that we're trying to address. This foundation makes everything else I'm about to show you work 10 times better. All right, let me show you the six gamechanging hacks that will take this foundation and turn it into something incredible. Hack number one is the truth detector. This is the hack that prevents embarrassing AI mistakes that could damage your reputation or cost you money. Most people don't realize that AI sounds confident even when it's completely guessing. So, I force it to rate its own confidence. I add this to every important prompt. For each claim, rate your confidence. Virtually certain, 95% and above. Highly confidence, 80 to 95%. Moderately confident, 60 to 80%, speculative, or low confidence, below 40%. And explain your confidence level. Let me show you this in action. I'm going to ask it something it may not be entirely sure about. Let me use the framework we just learned. You are a marketing consultant with 10 years of experience in restaurant marketing. That's our role. Client is asking about strategies to increase foot traffic and online orders for their restaurant. That's the context. Provide five strategies with explanations. That's the task. Return as a numbered list with brief explanations for each strategy. That's the format we're looking for. Do focus on proven methods and emerging trends. Don't include outdated tactics. That's the rule. That's our constraint. And for each strategy, rate your confidence. This is where I'm going to put in what we just talked about. virtually certain, highly confident, etc. And let's see what it comes back with. See how it's telling me it's virtually certain about strategy number one. Then it moves to highly confident for strategy number two, and then down to the last couple, four and five. It's only moderately confident in those ones. Now, the AI can tell me when it's basically guessing. This has saved me from so many embarrassing mistakes. No more blindly trusting AI when it's just making educated guesses. Now that you know how to catch AI when it's making things up, what I'm about to show you will completely change how you create prompts in the first place. Hack number two is the AI prompt helper. This is such a game changer. Another method that I use when I'm struggling with prompts is I get AI to help me with the prompting itself. There's two awesome ways to do this. Approach number one is starting from scratch. Instead of trying to figure out the best way to ask for something with a prompt, if I was looking to use AI to help plan a weekend trip, I could simply say, "I want to plan a perfect weekend getaway for two people on an $800 budget within driving distance of Chicago. Write the optimal prompt that I should use to get the best travel recommendations from you." Look at this. The AI just wrote me a nicely structured prompt with context, specific instructions, the output format, and it knows how it wants to be prompted. And now I'm going to use the prompt that it just gave me. I'm going to copy and paste that right into the prompt box. And here's what we get. Amazing, right? It's given us some really good recommendations in here with things to do, where to stay, the cost breakdown, how long the driving distances away from Chicago. Perfect. This is great. Now, here's approach number two, and this one's for when you're frustrated with your AI results. Let's say you already wrote a prompt, but you got disappointing results. Instead of getting frustrated and trying to guess what went wrong, I'm going to say, I tried this prompt. Plan a weekend trip for me and my friend. We have about $800 to spend and want to go somewhere within driving distance. Give me some ideas. But the output wasn't great. Can you analyze my original prompt and improve it to get a better result? And look what it generated. It's identifying exactly what was missing. Too vague on location, no interest mentioned, no context on travel dates, no preference on the vibe, and then it gives us an improved version of the prompt where we can fill in additional details that we were missing. Whether you're starting from scratch or fixing what you already have, AI becomes your prompting partner. I use this all the time for everything from personal stuff, business strategy, content creation, and even data analysis. Instead of trying to guess why I didn't get the best result from a prompt, I just ask AI for feedback on the prompt itself. But if you thought that having AI write and improve your prompts was next level, then wait until you see what happens when you stop using the same AI model for everything. Hack number three. Here's what separates the AI beginners from the pros. Your AI tool of choice has different models that excel at different things. And the model that you choose can significantly impact the quality of your output and your results. The secret is understanding which model does the task that you're asking it to do the best. Let me show you what I mean using chat GBT as an example. So, if we look at ChatGpt, it has a bunch of different models. The 40 is great for everyday tasks. The 4.5 excels at tasks requiring a lot of emotional intelligence, like creative writing, for example. The 03 and the O4 Mini are built for more deeper reasoning and complex problem solving. The 4.1 is great for analyzing information and great for business strategy. And the 4.1 Mini is perfect for just quick, straightforward requests. Let me show you. So, I'm going to give it a prompt using our six-part framework with the GPT 4.5 model for a creative writing task. Here's the prompt I'm going to give it. You're a published novelist known for emotionally resonant literary fiction. A reader has just found an old yellow tucked letter behind a loose floorboard in their grandmother's attic, etc., etc., and write opening paragraphs that capture the moments of the discovery. Don't rush to reveal what the letter says, example tone, and it's very descriptive kind of language. So, at the end of the prompt, we give it an example of what we're looking for in terms of the quality of the writing. All right, we're going to generate and see what it comes up with. Wow, look at the result. You could see it's very descriptive language and it really captured the example that we gave it really well at the end of our prompt, the example tone, and it really does sound like something you'd read in a novel. Now, I'm going to use the exact same prompt with the 40 model, which is more for kind of everyday uses, multiple different types of uses, and see the comparison between the two. Now, this one's pretty good, too, but when you look at the words it's using, it's not nearly as descriptive or dramatic as what we got from the 4.5 model. So the key takeaway here is that the model that you select can have a significant impact on the result that you get. And the same principle applies to whatever AI you're using, whether it's Claude, Perplexity, Gemini, or other AI tools. They all have different models optimized for different tasks. You wouldn't use a chef knife to flip pancakes, right? Using the right model for the right job makes a huge difference. Now, that hack was powerful, but if you don't combine it with what I'm about to show you next, the way to make AI its own editor, then you're missing the real magic. Hack number four. This one's my favorite, and honestly, it's like discovering that AI has a hidden superpower. I accidentally discovered this when I got frustrated with bad output. Instead of starting over, I asked AI to critique its own work. Let me show you this in action. I'm going to start with a LinkedIn post request using the framework that we talked about. You are a career coach helping professionals advance in their career. A mid-level professional in their 30s wants to share insights about networking to build their personal brand on LinkedIn. Write a LinkedIn post about the importance of networking for career growth. Return it as a 150word post with an engaging hook, a couple actionable insights, questions to drive engagement, make it personal and authentic. Don't use corporate jargon or cliches and I give it an example tone and we're going to see what it comes up with. Okay, great. Here's the first draft. It's pretty good actually because we use the framework and it's structured, has the right tone, includes actionable advice. But watch what happens when I use the self-improvement loop. Now, I'm going to say analyze your previous response and identify three specific weaknesses. Then rewrite it addressing those issues. Do this three times, focusing on different aspects each round. Look at this. It's identifying its own weaknesses. The first draft, surface level vulnerability, lack of emotional stakes, missed opportunities to reflect on the outcome. And then here's version two. Much better, right? Looks better. Tone seems to be better. for improved storytelling and emotional engagement. But we're not done. Now, here's the output from round three. This one was focused on the call to action and engagement. The AI basically becomes its own editor. Look at the progression. We went from good to great. Now, we have a bunch of different versions that we can review and decide which one we like best. Or we can just ask AI which version is the best, like this. In this case, it liked round two overall because of the storytelling and the relatability, but it also liked round 3's call to action. So, at the end of the output, it actually asks us if we want to create a hybrid version that combines the strengths of the round two and three versions, which I I think is a great approach. So, instead of just accepting the first version's output, you're forcing it to keep improving. It's like having a built-in quality control team that never gets tired. Now, what if I told you that there's four simple words that can significantly increase the accuracy of any strategic or complex task. Hack number five is the four-word miracle. Add these four words to any strategic or complex prompt. Think step by step. That's it. But don't underestimate it. I've tested this out on a lot of prompts and it consistently gives you better, clearer, more reliable results, especially for things like business planning, marketing strategy, and content creation. Why? because you're asking it to show its thought process instead of just jumping straight to the answer. Let me show you. Here's a strategic planning request. I want to create a content strategy for my fitness coaching business. I post on Instagram three times per week. I have 5,000 followers and I want to increase engagement. My audience is busy professionals 28 to 40. Create a 30-day content plan. Now, when it generates the response, it gives me recommendations, but I don't see the thinking behind them or how everything connects together. Now, let me add those four magic words, think step by step, and we'll see what it comes up with. Wow. See, look at this big difference. It's showing me the strategic thinking step by step, the audience analysis, content pillars, posting schedule, engagement tactics. Each piece builds on the last, and I can actually see the thinking behind every recommendation. Now, some of you might be wondering, well, why not use the 03 model? Doesn't it show its work automatically? And that's a great question because yes, there are more advanced models like the O3 model that do often provide built-in reasoning. But here's the thing, not everyone has access to those models. And for tasks like content strategy, like the example that we just went through, they're often overkill based on my experience. So if you're using the free version of Chatubt, or you just want to keep things simple, this trick gives you a lightweight workaround for better, more strategic results. This can work for business planning, calculations, decision-making, anything where you want clearer thinking without the complexity. That four-word trick is incredibly powerful for reliability, but this next hack is my secret weapon for getting strategic answers you'd normally have to pay a consultant for. Hack number six is the priming trick. Instead of jumping straight to my specific question, I'm going to ask a broader question first to activate all the AI's relevant knowledge. Let's say I was creating an online Excel course for adults and I wanted some help with the course structure and the program. First, I'm going to ask it this. What psychological and educational factors make online learning most effective for adult learners? And here's what it generates. Wow, look at this response. Very detailed. Talks about the autonomy of the adults, self-direction, internal motivations, practical applications, confidence building. Really good insights for us to then use for the next prompt. So, now that we've primed AI's knowledge and it has all of that great information in its memory, we're going to follow up with another prompt. You are an instructional design expert specializing in adult education. I'm creating a 2-hour online course about Excel for small business owners who are intimidated by spreadsheets and have limited time. Based on the learning principles you just outlined, provide some key strategies for my course, content delivery, and examples. Return as a structured plan with five key recommendations. and focus on reducing overwhelm and increasing confidence, etc., etc. And here's what it comes up with. Really, really good information. So, it's taking information from the previous output that we got from it, psychological factors and things like that, and it's addressing those things within this prompt, which is amazing. Start with a confidence building quick win, organize content into bite-sized standalone mini lessons. Lots of those insights that we got from the previous prompt are included in here. The AI basically downloaded its knowledge of the adult learning psychology first and then it applied it to my specific situation. So this approach gives us way more strategic thinking that considers the psychological part of it rather than just quick tips. And this priming technique works for any complex topic, business strategy, technical problems, creative projects, you name it. So now you've got these powerful techniques, but here's the trap that catches 90% of people. They get one good result and they think they're done. Real prompt engineering means testing prompts multiple times, finding patterns in the failures, iterating and refining until it's bulletproof. I have a library of my own tested prompts that work every single time. It took time to build, but now I can handle some of my most tedious tasks in seconds. Now, when I need to write a piece of content or an email, I don't cross my fingers and hope for the best. I use a prompt I've tested multiple times that I know delivers exactly what I need. And for really important decisions like strategic business choices or anything that could impact my reputation, I'll even run the same prompt across multiple AI tools and have one of them critique which response is the best. That's the difference between just celebrating single wins and actually starting to build systems. So here's what I want you to do right now. Pick one thing that you ask AI to do regularly. Could be emails, creating content, analyzing data, whichever task. Build a prompt using the six-part framework that I showed you. Add in a couple of the hacks I showed you and then test it a few times. Refine it until the output is actually something that you'd use. Do this for just one prompt and you'll understand why some people are getting incredible results while others are struggling with AI and you'll join that small group of people that actually knows how to leverage these tools properly. And trust me, once you experience the difference, you'll never go back to basic prompting again. If you enjoyed this video, then please show me your support by hitting the thumbs up button. It really helps the channel. And if you want to learn more about how you can use AI to level up your business and your life, then click this next video.