
Systemize Success: The ‘Who, What, Why, How, Now’ Method for Real Estate AI
Welcome to another exciting episode of The Real Estate Growth Hackers Show! Today, we have a game-changing discussion lined up for you as we dive deep into the world of Artificial Intelligence (AI) and explore how it can help you achieve your real estate goals. Whether you’re a seasoned investor or just starting out, this episode will provide valuable insights on how to unlock AI’s power and get the results you’re looking for.
Understanding the “What, Why, How, Now” Framework:
AI has become a buzzword in the real estate industry, but many people are still unsure about how to effectively leverage its power. That’s why we’re here to break it down for you. In this episode, we discuss the key ingredients to getting what you want from AI, starting with the “what, why, how, now” framework.
The “what” refers to understanding what AI can do for you and your real estate business. It’s crucial to have a clear vision of your goals and how AI can help you achieve them. We’ll explore the various applications of AI in real estate, from predictive analytics to virtual assistants, and everything in between.
Next, we delve into the “why” behind using AI. Understanding the potential benefits and advantages of incorporating AI into your real estate strategy is essential. We’ll discuss how AI can streamline your operations, enhance decision-making, and ultimately drive growth and profitability.
Moving on to the “how,” we’ll provide practical tips and strategies for implementing AI effectively. From choosing the right AI tools and platforms to training your team to embrace AI technology, we’ll guide you through the process step by step. We’ll also address common challenges and offer solutions to ensure a smooth integration of AI into your real estate business.
Lastly, we’ll tackle the “now” aspect of AI. Timing is crucial when it comes to adopting new technologies, and AI is no exception. We’ll discuss the current state of AI in the real estate industry and provide insights on how to stay ahead of the curve. Whether you’re just starting to explore AI or have already dabbled in it, this episode will equip you with the knowledge to make informed decisions and take immediate action.
Conclusion:
As the real estate industry continues to evolve, it’s crucial to stay ahead of the game. AI has the potential to revolutionize the way we do business, and understanding its key ingredients is essential for success. Whether you’re looking to optimize your marketing efforts, streamline your operations, or make data-driven decisions, AI can be a powerful tool in your arsenal.
Don’t miss out on this game-changing discussion! Tune in to this episode of The Real Estate Growth Hackers Show and unlock the power of AI to get the results you’re looking for. Your real estate goals are within reach, and AI can help you reach them faster and more efficiently than ever before.
AND MORE TOPICS COVERED IN THE FULL INTERVIEW!!! You can check that out and subscribe to YouTube.
If you want to know more about Zach Hammer and Charlie Madison, you may reach out to them at:
- Website: https://realestategrowthhackers.com/
- LinkedIn: https://www.linkedin.com/in/zachhammer/
- LinkedIn: https://www.linkedin.com/in/charliemadison/
[00:00:43] Zach Hammer: Welcome back. On today’s episode, we are going to [00:01:00] be talking about The Key Ingredients That You Need In Order To Get What You Want Out Of AI.. So what we need to come prepared with, what we need to provide to tools like ChatGPT, various LLM’s, Claude, OpenAI, whatever you’re working with. I’m sure this stuff will continue to apply to some degree.
[00:01:19] And we’re talking about all those foundational pieces. In order to do that, is today with me, I have my co-host, Charlie Madison, the man, the myth, the legend.
[00:01:27] Today wearing a nice what’s that? What’s that pattern called? Plaid. Is that the right word for that? Plaid? I think.
[00:01:33] Charlie Madison: Good. Non Hawaiian. It’s Don’t hold it against me, please.
[00:01:37] Zach Hammer: You’re looking more Scottish than Hawaiian today. Let’s go that route, although I don’t know if they typically wear that pattern on shirts. I think it’s more on the kilts and stuff.
[00:01:47] But anyway, all that aside, let’s go ahead dive into this.
[00:01:50] I wanna give you a bit of a back story for this and like where my mind was going, around how to convey this. One of the things that I’ve been discovering and working with AI. That’s been like [00:02:00] this really interesting insight that’s been really powerful in terms of we’re in this wild west world of figuring out, nobody really knows exactly how to correctly work with AI.
[00:02:10] We have ideas and we’ve tested all sorts of things, but like the way that it’s built, it’s not built in the typical way that things are coded. It’s not like just a bunch of logic statements saying, when this happens, then do this. When this happens, then do this. Like it’s not that.
[00:02:23] It’s machine learning built where it’s essentially saying, here’s inputs and here’s example, successful outputs. I’m gonna feed you enough of these until you understand how those connect. And then I’m gonna start feeding you inputs without the outputs. And then you get as close as you can to those outputs until you get closer and closer.
[00:02:40] It actually writes itself in a way, right? Like the code writes itself in the process of just training it on this data. And so the end result is, we don’t completely know how it works. We only understand what we trained it on, and so we almost have to discover how it works through working with it, so that’s the groundwork.
[00:02:58] What’s really [00:03:00] interesting is that since these large language models are built off of language, what I’m discovering every time somebody seems to find a new way of prompting or structuring something with AI that’s getting good results, I’m very quickly seeing that it’s also the same things that tend to be required when you’re working with humans, that when you think about how do you make, convey something clearly to a human, how do you teach a human to do something clearly?
[00:03:29] Like, how do I train people on my team to do work effectively? All those same ideas that I’ve learned that make a difference on communicating clearly and teaching effectively to humans are what I’m seeing crop up in the world of AI as well. When you want to write a good prompt or build a good GPT, or like how you need to structure your data and all that it seeming to match very closely those same principles of how you work with and teach humans effectively as well.
[00:03:59] [00:04:00] And so that sort of gave me this lightbulb moment of, I have a framework that I use to teach humans, and I started to realize that actually maps pretty well to what I was already doing with my mega prompt framework and some of these other things that I was learning. And so I figured I’d apply that concept and see where that took me.
[00:04:17] And I’ve been really pleased with the outcome of giving clarity to something that feels like maybe a big confusing topic. So that’s the groundwork. Does that all make sense? Are you tracking with me so far?
[00:04:28] Charlie Madison: Yes. What I heard you say is you found there’s a really good framework, and you’ve shared it here before. I assume we’re going to share it today on communicating, teaching, communicating so humans actually understand it, right? Because there’s a difference between saying something and the other person understanding it.
[00:04:45] And what you’ve discovered is that the same framework works for humans and I’ve looked into this a little bit, and to me, part of it makes sense because AI is being trained on human [00:05:00] words, human thoughts, and so it makes sense that it’s evolving itself to learn the way that we learn and teach.
[00:05:08] Zach Hammer: Exactly. So yeah, the reasons why it ends up there are a backwards way to do it, but it ends up in a similar result where, because it’s trained on our language, the things that it connects ideas to tend to be the same ways that like us as humans connect ideas together. So yeah, it’s really interesting.
[00:05:25] And yes, so the framework that we’re talking about it, this is useful regardless if you wanna teach something and you want somebody to understand it. This is the framework that I’ve always used to not always, but I’ve learned to use. And that framework is the what, why, how, now. Okay.
[00:05:40] And so laying out what that is just at a basic level, the what is giving people context, it’s laying the groundwork, making sure people are starting on the same page, providing definitions to any words that might be confusing, making sure that when somebody’s approaching this you’re giving ’em the context to be able to move forward. Okay. That’s what is about.
[00:05:56] Why is, why it’s important. It’s all the reasons of why does [00:06:00] this matter? Why does it matter? Now, why does it matter to you? Why does it matter to your goals, right? Like why does this matter? If I’m doing training in my business for my team, why does this matter for the business? Why does this matter for your role in light of the overall company, right?
[00:06:13] So the why, because that tends to be necessary for people to then be able to understand the right context, both the what and the why for the next step, which is where most people try and jump right into. But if you’ve done that, you’ve probably found that it didn’t work well.
[00:06:29] And so the next thing is the how. This is what most people try and just teach. They just show, this is how you do it, step by step you start here, you do this, you need these things, but without the what and the why, people tend to get lost ’cause they don’t understand why they’re doing this. They don’t understand the context.
[00:06:45] Their brain isn’t set up to receive the steps properly. And within the how, the what, why, how now just let you know fractal in that within each step of the how it can be useful to also go through what, why, how now. For [00:07:00] each of the steps in the how, right? So that you’re giving the context of why, what is this step about? Why does it matter? Then how do you actually do this step?
[00:07:07] And then here’s now an example. So that’s the next part in the overarching process too, is the now is stories, metaphors, examples, templates, case studies, that sort of idea. It’s also where you typically say, now that you’ve learned this, here’s what you should do next.
[00:07:24] So that’s what, why, how now is it’s context, the reasons why it matters, especially how to do it. The step-by-step process and then now is examples and ways for somebody to take it and move forward. The examples are important because it helps to make it clear to a person, you’ve explained the concept.
[00:07:41] Here’s some examples that your brain can flesh it out and put more meat on the bones of what you’ve explained to see it in practice in a couple of different ways. That unlocks a level of understanding that’s different than just explaining it exactly. And that’s the framework.
[00:07:53] But here’s what’s interesting is that I was figuring out, so if you’ve followed any of my stuff about AI [00:08:00] lately you’ll know I talk about the mega prompt framework. And the mega prompt framework has a lot of similar elements, right?
[00:08:05] So we give it what, we’re telling it what the task is, we’re telling it what we want, we’re telling it what it needs to look like, why a little bit about why we’re doing it. Like why is the, what’s the goal? So the goal is why you’re doing it, why you’re working toward this, right? The how we give it the steps. We also give it a lot of specificity on the how, with the context and constraints to say, here’s how I want you to give something back to me.
[00:08:29] So we’re covering a lot of the same information, but I realized there’s something that we do in the mega promp framework and in my AI stuff as well that was missing from this. So that’s made this even clearer. Let’s dive into now the overarching framework, for when you wanting to work with AI, like how this works.
[00:08:46] So some of these elements that we just talked about are still gonna be necessary, but I wanna break it down from the beginning so that you could see where they fit and how it ends up, getting you to the same place.
[00:08:54] Does that sound good?
[00:08:55] Charlie Madison: That sounds good.
[00:08:57] Zach Hammer: Okay, so first off, I realized that when it [00:09:00] comes to AI, part of the context that’s necessary for it, that you really wanna define very clearly. And this is potentially true for the training, but for whatever reason, it doesn’t come up as necessary as often when you’re teaching, but it’s really necessary with AI, and that’s the who.
[00:09:17] So our framework for AI is who? What, why, how, now? Okay. So who, when I say who, what do I mean anything that’s dealing with the people or who I want the AI to act as. So who is three things? One, who should the AI Act as? Who should they take the role on as if they’re gonna do some sort of task or process for you? Like Charlie, gimme an example of a task or a process that you might have AI do. And I’ll give you an example of a who that we might have them be.
[00:09:49] Charlie Madison: Take a video transcript and turn it into a blog post.
[00:09:54] Zach Hammer: Okay, cool. So the who for something like that, I’m gonna think of either a very specific [00:10:00] person, somebody who I know. Does that work well already? And I’m gonna say, you’re gonna act like this person, that this would need to be like a public figure. So somebody like that maybe. Yeah, exactly right.
[00:10:09] Or maybe it’s like you are Tim Ferriss ’cause I know Tim Ferriss often has both videos and blogs to go around topics. And so you might go that route, typically I tend to go more toward a job title and say the who is what kind of role. So that might be you are a copywriter specializing in turning the concepts from a video into a well-structured blog post that achieves these sorts of results. So like I’ll be very specific on the who and say essentially I’ll give it a job description, right? Of like who you are, what your skill set is, that sort of idea. It’s important for AI because it lets it narrow down its vast knowledge down to a specific subset field and zero in on that. So that’s one of the who’s. Who is the AI?
[00:10:52] The next, who is, who are you? So this isn’t always necessary, but it’s often really valuable to make sure that AI has [00:11:00] the context of who you are in order to then either splice in mentions of your company of your products, of those sorts of things where it makes sense in the vein of what it’s gonna create for you. So the who answer for me is often less who am I specifically?
[00:11:15] It’s all the relevant details of my company is real estate growth hackers. And what we do is we help real estate professionals to be on the cutting edge of marketing to generate as many engaged activated leads as possible, leveraging new technology like AI to make that process as effective as possible.
[00:11:33] So like that’s what we do and I’d give it a bit more context. I might mention in my who documentation around real estate growth hackers, I might mention some of the key ways that we help with that. Like we do that through free training, communities, blog posts, courses, as well as tools and templates, right?
[00:11:50] So like I mentioned it like a high level, this is like what we’re doing in that vein. Okay. I might even mention some things like what our track record is, some of the success that we’ve had, things that might show up in [00:12:00] like a bio, so it has some understanding of who we are, the level that we’ve succeeded, all of that.
[00:12:03] And it may not use any of that copy in what it’s gonna write for you, but it gives it context to know, I’m writing on behalf of this company, this is what I need to know about this company. Similarly if you’re like creating a presentation, you’re, who might be more information about you. What have you done personally? What are the things that matter to you? Do you have a family? Do you have kids? Are you married, not married? Sometimes that stuff’s relevant, sometimes it’s not. So, you zero in on what would it need to know about you that would be relevant to whatever the context is that you’re looking to go into.
[00:12:32] And this isn’t something that’s I feel like this is fairly intuitive, if that makes sense. It’s not something that there aren’t a ton of massive surprises here, right? What’s relevant to think about yourself in light of a blog post geared toward real estate agents?
[00:12:44] I probably don’t need to go into my entire life story except for the points that might be relevant to that. Does that make sense? So when we’re thinking about who we are that’s what we think about.
[00:12:52] The other who that’s necessary. So far we’ve covered who is the ai? Who am I? The next one is, who are you serving? And [00:13:00] the who information around them is going to be things like, who are they? Who’s your target market? What are their pain points? What are their struggles? What are their aspirations? What are their goals, dreams? What are things that keep ’em up at night? What are they currently struggling with in light of the things that your company, product service might help them with?
[00:13:17] Getting that information documented and provided to AI in a way, that gives it, that context really helps to be able to get back stuff that’s relevant from your company to your target market in a way that’s really relevant and zeroed in on those key ideas that actually matter. Yeah
[00:13:36] Charlie Madison: Three who’s.
[00:13:37] Zach Hammer: Go ahead.
[00:13:38] Charlie Madison: The three who’s? Who is ai? Who is I, Who am I and who am I serving?
[00:13:45] Zach Hammer: Exactly. And what I found is, I often think of those as pieces and components that I will reuse, right? So I spend time developing documentation around who am I? Who is my company?
[00:13:58] I have documentation [00:14:00] around who am I when I am presenting myself as a speaker? Who am I when I’m presenting myself as somebody you should listen to on training? Who am I in terms of my results? What is my company? Those sorts of ideas, and I keep those as documents that I can pull up and reuse when I want to engage with AI in these different components, same thing, I use, I create documentation around who my customer avatar is or customer avatars are, right?
[00:14:25] And I’ll keep that documentation and I’ll do extensive documentation, but the other thing that I’ll do is I’ll create summarized key point versions as well, so that they’re more able to be leveraged with AI as needed in those different contexts, right?
[00:14:38] But each of those things, they’re like building blocks that I know, I keep, I build a library of them and I pull them in and use them as is necessary, depending on the context. Does that make sense how those components they get reused as you apply different tasks? Where, you might build a version of AI that’s designed to do a specific thing, but it’s gonna reuse who you are and who [00:15:00] you’re serving and just apply that to maybe blog posts versus social media posts versus video template versus email versus ad, right?
[00:15:07] So those like aspects of your overall structure might change, but who you are and who you’re serving probably doesn’t.
[00:15:13] So you reuse those. Does that make sense?
[00:15:15] Charlie Madison: Yeah. What I like about it is, if you just say real estate agent to someone.
[00:15:19] Zach Hammer: Right,
[00:15:19] Charlie Madison: That person could mean a bunch of different things. It could be a teacher that sells real estate part-time, could be someone that owns a brokerage, it could be someone that runs a team, it could be a solo agent or a husband, wife, it could be someone that sells condos or new construction.
[00:15:37] Like it’s one word to us, but there’s a whole different world of context behind that. And if my coach Joe says if we don’t give AI the context of what we mean in our world, it will go out and make it up. And some of it might be right, but some of it might not.
[00:15:58] So what I’m hearing you is, the [00:16:00] word is real estate, but what does that actually mean? Like your focus is mainly, brokers that act as teams and so to be able to flesh that out and even what does that mean? AI by default thinks team, and so, it’ll have some pieces, but teams are different, and so to the more context you can give it, the more it can use its magic to have that message market match.
[00:16:24] Zach Hammer: Exactly. And yeah ’cause you’re when in the real estate industry, when we think real estate agent, we’re often thinking residential real estate agent reselling properties for sale, right? Like that’s the typical thing, but real estate agent in New York City probably more often means somebody that’s dealing in rentals, right? And leasing, and then further, there’s a big question of are we talking commercial or residential?
[00:16:49] Because they’re different things and what I found is I found that when like the default, so you know what, like AI goes out and grabs when you just say real estate, it thinks that you’re [00:17:00] typically like just as often talking about investing and getting a return on your property as it does what the real estate agent typically does which you specialize in helping investors, that’s often the average real estate agent seems to be doing.
[00:17:14] They’re more often helping families, individuals, acquire a home for themselves to live in. And maybe there’s like an investing an component, but it’s not average person isn’t talking about how many doors they own. That’s investor language anyway.
[00:17:29] So yeah, being really specific on that matters a ton. And when you’re going into that documentation, I actually have tools and systems that I’ve built out so that you build those out in depth of thinking through a really fleshed out customer empathy map is what I call it, where you’re understanding what matters to them? What are they striving toward? What are they struggling with, right? What are they trying to achieve and what’s preventing them from getting there? And then how does your product, solution service fill that gap in order to get them to the next spot?
[00:17:58] And whether you’re looking to recruit [00:18:00] agents or whether you’re looking to go B2C and get people who are looking to buy or sell properties, you need to understand those elements of those different people. And be able to provide that to AI to get the best results back. So that’s the Yes.
[00:18:14] Charlie Madison: That customer empathy map, that’s one of my favorite things that you’ve got one of your tools. Because it’s stuff that we automatically know and I by default think that AI will find it. But, this is one of those things my customer’s empathy map is different than someone else’s that’s even serving the same the same group.
[00:18:35] We’ve been talking about how, you’ve gotta do something unique. And so like it’s, I’ve noticed I’ve gotta be very specific on the problems I solve or else AI will create generic answers that don’t actually speak to my client.
[00:18:51] Zach Hammer: Exactly. And so, this helps a ton for sure, and so that’s the who. I over time like this is still a somewhat fresh concept for me of applying it this way. [00:19:00] So I might add more to what I think of for who later, but right now, yeah who are you AI? Who am I? And who do we serve? Those are the answers there.
[00:19:08] So then, we go into the next step, which is what? So we’re providing it, the what are we looking for? What are we talking about? What are we trying to accomplish here? And that typically is just a simple description of the task or the goal to me.
[00:19:21] That’s just the high level context of what we’re doing is we’re creating a social media post. What we’re doing is we are defining a content strategy. What we are doing is we are writing an email, right? Like it’s the really high level just. Simply put, what are you looking to accomplish?
[00:19:36] And then the why is typically what are we doing so that this thing could happen? Right? So we’re writing an email so that somebody, books an appointment on our calendar, so that somebody clicks through to engage with the webinar, so that we build up the trust in the market, right?
[00:19:57] So that that’s the why. The what and the [00:20:00] why, the and the other aspect of what good why for AI is giving it a clear goal, right? So the task is we’re writing this blog post so that does this. Right? The goal is essentially you’re taking the AI into the future and you’re saying, when we’ve done this successfully, this is what it will look like. It will be a well-structured post that successfully drive somebody to the click. Does that make sense? We’re really clearly defining what does success look like? Does that make sense?
[00:20:27] Charlie Madison: That does make sense.
[00:20:28] Zach Hammer: Perfect. So what’s nice is that often, so far, at least I haven’t experimented with this a ton. With humans, we often need to go really deep into the what and the why. With AI that’s been less the case, right? As long as you could give it a high level what we’re doing and why we’re doing it. It fills in a lot of the why and why it matters and why it’s important.
[00:20:51] Based off your who. Who are you serving? And why would it matter to them? Why would it matter to your business? It can read between a lot of those lines when you give it the proper who. So with [00:21:00] humans, when I’m giving what and why, I often have to go step by step into definitions. AI doesn’t really need your definitions as much unless you’re using a term in a way that’s nuanced, right?
[00:21:10] So if it is, then give it that, but if it’s not, if it’s fairly on the service, like, it knows what an email is. And if you say a cold email, it’s gonna know that a little bit better than the average person too. That’s part of what’s useful about working with AI is that you can often do that. So okay. So we covered who? We covered what? We covered why?
[00:21:26] The next one that we do when we’re teaching is we do, how. So, how is the step-by-step process? It’s all of the specifics around it’s the meat of what you’re actually doing, right? So when you’re teaching a person the what is literally, first you go here, you click there, you download this thing, you process it in this way. That’s the how. It’s the same for AI.
[00:21:43] We provide it steps. We say, first I want you to take in the transcript, right? So let’s go back to our example of a transcript, a video transcript that we’re turning into a blog post, right?
[00:21:53] So first I want you to understand the transcript fully. Second, I want you to summarize the key takeaways from this [00:22:00] transcript such that somebody could take action on the points from the video. Three. I want you to take those key points in summary and turn it into a well-structured article that achieves this end result.
[00:22:10] Now, the reason why we do that, is because the same way that, if I told you Charlie let’s take somebody other than you, somebody that isn’t already educated in this process.
[00:22:19] If I pulled somebody off the street and I said, Hey here’s a transcript for my video. Turn it into a blog post. I’ll pay you money to do it, but do this, do I expect that the results are gonna be very good? Probably not.
[00:22:32] Charlie Madison: Yeah.
[00:22:33] Zach Hammer: But if I take that same person and I say, Hey, and I take ’em through the whole, what, why, how, now, and in the, how I explain, I need you to take this video and turn ’em into a blog post for me. Here’s how I want you to do it, go through, understand the transcript, pull out a few of the key points, and then use those key points as an outline for what you’re gonna write.
[00:22:51] Assuming this person has some level of skill set writing. Like from that they might actually be able to do a pretty good job because I’ve guided them along the path of how to think through it. [00:23:00] The other reason why this works is because, again, going back to that high-level concept of AI seems to work like us. Charlie, when you are thinking through, your best course of action and how to do something well. This may not always feel natural to do, but where do you feel like you make a better decision when you only have one idea in mind or were laid out in front of you?
[00:23:22] You have multiple options, and from those options, you’re choosing the best path to move forward which of those seems to work best for you?
[00:23:30] Charlie Madison: In the moment, I feel like if I’ve got multiple options for me, I kinda sift and sort my way through it, so to speak.
[00:23:38] Zach Hammer: And so, that tends to help to get to a better outcome where you can see you gain clarity by seeing this is bad and here’s why this is better and here’s why, because I could compare, right? So, or similarly if you’re presented with do this thing, here’s the really rough version, versus do this thing, and then here are the processes to take it and [00:24:00] refine it and turn it into the thing, you’re more likely to get a better result by making a smaller leap each time. We’re taking rough and turning to summary is easier mentally than trying to think through the whole process and one go. It’s true for humans, it’s true for AI when you have it, go through a process where it literally it’ll write this stuff out for you if you tell it to where it’ll pull out the key points.
[00:24:22] It will write the summary, and then from that information, it has better context to actually go through and write your blog post. So we lay out the steps to get it to think that way, to think in building blocks, to think in logical next steps that are well-reasoned rather than, here’s a really rough thing, go to the complete thing right away.
[00:24:41] Like it can do that at times, but you get a better, more consistent, more reliable result when you lay out that path that you want it to follow, and you’re essentially really clear in doing it. Does that make sense for kind of that idea of breaking down the how?
[00:24:54] Charlie Madison: Yeah. And in my world, you know what I’m thinking is it is the difference between [00:25:00] systematically following what’s been laid out in creativity. Like in creativity, I like having lots of different options and I figure it out. But then once I’ve got my system, I want the system to be followed each time.
[00:25:11] Zach Hammer: Right. Yeah, that makes sense. You cut out there a little bit on my end, but yeah, I think the ideas that you’re saying definitely makes sense. We had three different, who’s right? So who am I? Or who is the AI? Who do I serve?
[00:25:22] We have multiple things in the how as well. So for the how, it’s the steps is like how do you do it step by step. Okay. That’s the first part of the how, the other part of the how is, how do I want it to sound? And how do I want it back in terms of format? So how do I want it to sound is essentially anything that you want to do to convey writing a blog post, like we just gave some good examples, right?
[00:25:48] So Seth Godin, sounds different than Tim Ferriss, and I imagine, I don’t know if Joe Rogan writes much for blog posts, but if he did, he would sound different than either of those guys [00:26:00] do.
[00:26:00] And all of those people sound very different than, I don’t know if who’s a famous writer. If J.K Rowling, who’s gonna write a blog post, she would sound very different than any of those people,
[00:26:10] Charlie Madison: yeah.
[00:26:10] Zach Hammer: And so when we’re thinking about how do I want it to sound, we’re thinking tone, we’re thinking the feel. What are those ideas? And similarly, you could be vague with that and say, I want it to sound professional, I want it to be friendly, I have some phrases that I know for my style, I really like some words that sort of get a good result.
[00:26:26] But when like wanna be really specific, this is one of those other areas where I put together documentation that explains, this is what the style is and I document it and lay out multiple points about what I mean that it’s friendly, the sentences are succinct. I use lots of line breaks.
[00:26:43] So like I’ll lay those out and make that really clear. So that’s gonna be part of the how I want it to sound. You could go somewhat simple on that and find the words that tend to get you back what you’re looking for. If you’re really clear on what you want, you might even build out documentation. There’s actually more of that in the now. So, that’s the how do I want it to sound?
[00:26:58] The other thing is [00:27:00] like how do I need it back? And that’s gonna be your constraints in terms of is there character limits? Does it need to come back at a spreadsheet? Does it need to come back as code? Does it need to come back formatted with markdown? Does it need to come back using bolding or not bolding like some of those things, right?
[00:27:13] Like any of those constraints where, like as for instance, by building an AI that’s gonna write MLS descriptions. Those MLS descriptions almost always have a character limit.
[00:27:22] So, that’s the other thing that I’m going to be baking into this is that expectation. Now do note things like character limits AI’s not perfect at, so it’s gonna get you in the rough ballpark. It can understand the entirety of human language. But basic arithmetic, it still struggles with a bit in terms of counting its own characters.
[00:27:40] It’s one of those interesting quirks of a large language model currently. But so do note it, it’s gonna give you rough things anyway. But
[00:27:46] Charlie Madison: I’ve got a question about that.
[00:27:48] Zach Hammer: Yeah, go ahead.
[00:27:49] Charlie Madison: I wonder if you ask it to provide in parentheses the count of the characters next to it.
[00:27:55] If that would actually help it more.
[00:27:58] Zach Hammer: It might, what’s [00:28:00] interesting, one of the guys that I follow, that’s literally one of the things that he tests every model on. So he goes through and he tests a bunch of different models. One of his questions is what is the number of characters in your reply to this question and add?
[00:28:12] There’s only been one that’s gotten it right. Yeah, every single one of them gets it wrong. Some of them get it like way wrong. But yeah, for whatever reason, it struggles with. And part of that’s just how they work. They’re large language models are designed when I have these characters, this is what I predict comes next.
[00:28:27] And accuracy on character count is not necessarily a language thing, it’s a different part of, anyway. But yeah, so the steps, the how do I want it to sound? And then any other specific constraints, right?
[00:28:40] Like I want three of ’em. I want I need ’em to be this short, anything else that you could think through where you’re being really specific, here’s what the delivery should look like. Does that make sense on how we clarify that highway area?
[00:28:51] Charlie Madison: That makes sense.
[00:28:52] Zach Hammer: Perfect. And so, then the last one is the one that people typically miss, right? When we listen to like people talking about prompt engineering [00:29:00] and how to structure this? I have found not only, this is also the thing that people tend to miss when they’re teaching, and it’s the thing that they tend to miss when they’re working with AI.
[00:29:09] People learn better when you give ’em examples, stories, and ways to flesh out the concept. And AI works better when you give it specific templates and examples of what you mean when you say something. And so, now is examples, now is stories, now is case studies, now’s what I want you to do next, right?
[00:29:26] So in the Who, what, why, how, now for AI it’s really useful to document, if like the structure that you want an email to do. And by the way, I recommend thinking through that rather than just trusting AI to come back with something that’s good.
[00:29:41] So, when you know that structure, you provided a template that explains the concepts of, first line I open with a friendly greeting, next line is something relevant to that person personally. The line after that is contextually related to the topic that I wanna talk about, phrased as a question and a hook. Then I go to this, then I go to that, like when [00:30:00] you break something down as if it’s a template that somebody could rework and reuse.
[00:30:04] And you explain what each line, what each word is designed to do conceptually, AI can use that and run with that really well, but it doesn’t work nearly as well to do that alone unless you also provided an example of that template completed, right? So if you give it a template for an email and you say, this is how it works, here’s the example of what that looks like when it’s complete or examples, right?
[00:30:28] And so that’s the other piece that people tend to be missing. They’ll ask for, they’ll you’re the newbie AI prompting will be like, give me a social media post for real estate agents about this thing. And what you’re gonna get back is gonna be vague hot garbage, right?
[00:30:46] Charlie Madison: My favorite.
[00:30:50] Zach Hammer: Hey, vague hot garbage, just what I was hoping for. But if instead you say you are an expert social media copywriter, [00:31:00] I am a business that serves real estate agents in these ways. The real estate agents that I serve are these people. What I want you to do is I want you to write a well-crafted social media post that is designed to get them to sign up for my newsletter where I cover AI tips weekly.
[00:31:16] How we’re gonna do that is we’re gonna take this video that talks about how about this topic and we’re gonna use that as the hook in order to get them into this process. I want you to take the transcript, I want you to understand the summary. I want you to pull out the key points. I want you to take that and then to write an email about it based on this template, or not an email, sorry, we’re doing social media posts.
[00:31:35] I want you to write a social media post based on this template. Okay. I need it to sound like it was written by me, reference my documentation of how my social media posts sound, as well as the social media post template that I am providing. I need it to be this number of characters or less and I want three examples of it now.
[00:31:54] So I’m gonna provide it with documentation of who I am. I’m gonna provide it with documentation about who I serve. I’m gonna provide it with an [00:32:00] example or examples of what those social media posts look like, as well as or templates about what those social media posts look like and examples of those filled out.
[00:32:08] And what I’m going to get back, oh, and I’m gonna provide it, the transcript of the video that it’s using to promote. What I’m going to get back is going to be infinitely better. Then write me a social media post for real estate agents, right? And so when you think through it this way and you provide those and you build that out.
[00:32:26] And whether you’re doing that all in one prompt or whether you’re doing that by building GPT’s and building systems that have access to knowledge base, which by the way, that’s something that we talk about in our AI Mastermind for real estate teams, that how you can actually, instead of just writing a prompt that you’re actually building these tools as pre-trained systems to work for you so that you build ’em once and they just keep working for you.
[00:32:47] No matter which way you’re doing it, it’s all foundationally built on the same building blocks of the who, what, why, how, now. Does that make sense?
[00:32:55] Charlie Madison: It does.
[00:32:56] Zach Hammer: Awesome. And so there you go. That’s the process and what [00:33:00] we just did was also the who, what, why, how, now as well in terms of teaching this process.
[00:33:06] We talked about who this was for? We talked about what it is? And what we’re doing? Why it matters? We talked about how to leverage ? That’s where we broke down the who, what, why, how, now. And the now is the example that I literally just gave of how that process plays out when you’re applying it to a specific use case.
[00:33:21] The same way that hopefully helps you to understand this process. It also helps AI to get the same result. But there you go, so that makes sense.
[00:33:28] What do you think is most powerful for that for you? What difference do you think it might make for you in leveraging AI to get good, consistent results in your business?
[00:33:36] Charlie.
[00:33:37] You
[00:33:37] Charlie Madison: know, I think the insight that came to me is what would I do if I was onboarding an employee and they were an A player and I wanted to get them up to speed to produce the results that I wanted. And the cool thing about this is we can mold the AI to [00:34:00] be that A plus player. And that’s what all of these, and it creates consistent.
[00:34:06] Consistent yet variable output, which is really cool. And, the other big insight, I love the three who’s, I’ve heard you say who before and I knew about the who they, who AI is, but I had not thought of, I also need to tell them who I am. And who the customer is, like all three of those.
[00:34:29] And then I’ve actually got a question, which is, can I take one of my transcripts and tell AI, Hey, how would you describe my voice?
[00:34:42] Zach Hammer: That’s a great question. You can, so I’ve done this before, I didn’t do it with an audio transcript. I did it to try and extract my writing style from emails. So I have a specific way that I like to write emails. There’s some thought in it. There’s a flow.
[00:34:55] It feels the way I want it to feel. Keep in mind, by the way, as a quick [00:35:00] side note to this, it’s always worthwhile to note if you ask AI to do something, it will do it whether or not it’s the right way to get the right results. So it’s always worthwhile to actually test these ideas to see if they get you the result ’cause that’s the real thing you care about.
[00:35:15] You can get it to replicate what you do. You wanna test to make sure if what you do is actually what gets a good result. So anyway, as a quick site side note ultimately your goal is probably to, get responses, get replies, get sign ups, whatever.
[00:35:28] Make sure that what you do actually gets that in order to really build systems on it. But what I did is I uploaded a bunch of emails and I asked it to tell me back what are some of the key traits that you see of these? What are the things that you see done consistently?
[00:35:40] And this is actually the first time that I realized. This is where I learned this, that getting it to tell me that the style by itself wasn’t good enough because I did, I went through the list and I was like, yeah, I agree with all these things. This makes sense, this does seem to summarize how I write things and how I think about them, what things are present, [00:36:00] and I have that in a list.
[00:36:01] And then I took, like all of this is about testing. I took that and I took it into a different prompt and I gave it that and asked it to write an email about this thing. Using, in that style that I just developed and it didn’t give me back what I was looking for. It gave me back something that still matched that description, but it wasn’t quite right or quite clear.
[00:36:24] It wasn’t what I was looking for. And so what I figured out was necessary was I told it. Okay, so you’ve extracted these ideas from my emails. Now I want you to give me a list of the phrases from my emails that match that idea to convey it.
[00:36:44] So when I say friendly greeting, here’s an example of how I do a friendly greeting. When I say provide value in the email, here are examples of what that looks like, when I provide value in an email, [00:37:00] right?
[00:37:01] When I say short punchy sentences with lots of line breaks. Here’s an example of what I mean when I say that, and that’s where I first discovered if I just gave it the description without the example. The example conveys drastically more than the description alone because again, real estate agent can mean all sorts of things just the same way that short paragraphs can mean all sorts of different things. But when I show it, this is what I mean by a short paragraph, it gives it the clarity to know how to replicate that. But slightly different. And so yes, you could, what I found is it tends to take a bit of a process of iterating, finding the examples, and then restructuring that back into a document that has, here’s the breakdown plus the specific examples. And what you would want it to include is if you want it to sound like you in your example document, you also want to include examples of your phrases that match that idea so that it understands how to sound like you ’cause it needs that to [00:38:00] really understand it. Does that make sense?
[00:38:01] Charlie Madison: Yeah. This is my shameless transition. Do you go over this in your AI mastermind?
[00:38:09] Zach Hammer: I do. Yeah. So really what the AI Mastermind is all about is trying to take all of these concepts that we talk about here and make ’em as easy for you to implement as possible. We mentioned like you need to build out your understanding of your customers. You need to build out your documentation around yourself.
[00:38:27] Do you wanna see exactly how I do that for my business? Do you wanna see examples of how I built out customer empathy maps for real estate team owners who I am looking to reach? Do you wanna see the AI systems that I’ve built for how to do all that with AI assistance to make it as easy and scaled as possible, requiring the least amount of work from you as possible?
[00:38:46] Do you want to see my, at this point I’ve got at least 20 to 30 documented social media post templates that I’ve done so that I could use AI to help me write social media posts based on my contacts? Do you wanna see those templates [00:39:00] that you could use them and run with them and use ’em in your own business?
[00:39:02] All of that kind of stuff it’s what we’re doing. We’re not only giving you the tools to be able to take quick action on this. We’re showing you it in practice. We’re applying it to your business. We’re learning what are you working on? How can we help you leverage AI right now and get into the process of putting it into practical use? Rather than just the fun, flashy things that people are either trying to put it into practical use but not actually understanding how to use the tool, or they’re getting it to write raps about their cats, right?
[00:39:28] That seems to be the general structure right now. But what we’re doing in this AI Mastermind is we’re actually diving in and putting this stuff into practice, and I’m giving you everything that I have available to make that process as short, quick, and easy for you as possible.
[00:39:41] As we build things, we build ’em out, live together we keep adding more and more to this. If that sounds good to you, if you’re excited about what’s going on in the world of AI and you wanna know how to apply it in that specific scenario of being a real estate team, looking to recruit agents, being a real estate team, looking to provide enough business [00:40:00] for the agents on your team, being a brokerage that runs like a team and offers these kind of services.
[00:40:05] That’s who we’re geared toward, that’s who we’re looking to serve, that’s what we’re trying to do is make it so that you could achieve the goals that you have set for this year or future years and be able to do and scale effectively without requiring a ton of extra cost, without requiring a ton of extra manpower. Being able to le leverage the assets that you have right now to the fullest extent to get repeatable, scalable results that really leverage the power of this technology that’s available right now.
[00:40:31] So if you wanna do that, then I would be thrilled to invite you to go to RealEstateGrowthHackers.com/Contact. Reach out to us, let us know that you’re interested in the AI Mastermind and we’ll get you the details on.
[00:40:42] For one, if we have room right now, it’s not something that we invite everybody to.
[00:40:45] And two we’ll get you the details on how you can join us, what that looks like and make sure it’s a good fit for you.
[00:40:51] Charlie Madison: And I’ve gotta say, in this last week, I got access to the Mastermind vault. And I was as [00:41:00] giddy as could possibly be on Christmas. It was the, I spent a lot of money, a lot of time following AI on the newsletters, and so much of it is just thin. And some of it pretty good.
[00:41:14] But, you gotta go through pages and pages of blog posts. And so as your Google Drive opened up, it was like when Indiana Jones opened the arc of the covenant, it the sun shone down and I just saw ’em all there. And believe it or not, I had my team meeting this morning.
[00:41:31] And we were talking about SOP’s, and I was like, Ooh, hold on one second. And I went and I pulled it and I was able to take a transcript into an SOP. That’s just one of, I don’t know, however many. And as the one that’s seen it, if you want to actually have AI that works for you consistently. Like this is the flip a switch and it works.
[00:41:55] And then you actually help, I’ve been with you, you’ve been helping implement [00:42:00] it with these individual team owners and they’re giddy as well.
[00:42:03] Zach Hammer: Indeed. Yeah. I think that’s part of what makes it special is, you are right, like right now, most of the content that’s out there about AI is either really high level the people who are geeking out about AI but they’re geeking at it about the opportunities and there’s not a ton of people who seem to be applying it to specific use cases, specific needs.
[00:42:24] The people who are trying to apply it to specific needs are often trying to build tools that are designed for lots of different people. They’re building a tool designed to help you maybe write blog posts, or create YouTube transcripts. But again, they’re still missing that context of who you are, who you’re serving, all of that.
[00:42:41] And so what you end up getting back, it still ends up feeling like just as much work to get to a polished product as even if you just didn’t use it. I’m in this interesting bridge where, I know the real estate market. I’ve been working with real estate teams for the past decade and so I know the [00:43:00] needs, I know what’s actually needing to be done to get the work of real estate done in these environments.
[00:43:05] And I’m also geeking out about AI.. And so I am in this, in-between where I’m reading those blogs, I’m learning those concepts, and I’m figuring out where those things actually apply. In that gap to get the work of real estate done leveraging AI because there’s lots of hope and promise and opportunity that’s coming along, but it’s different when you actually try and apply it and see.
[00:43:27] Here’s what it could do someday versus here’s what it could do right now that actually makes a difference in saving you time, saving you money, being able to help your team get more results from less time being able to provide a higher level of service to your clients where you previously couldn’t. And yeah, so that’s what we’re doing.
[00:43:46] We’re applying it to that very specific use case and sure, leveraging lots of great foundational processes in the process, but you get like this pre-portioned thing, right?
[00:43:56] Where most of the AI world right now is like they’re handing you a [00:44:00] live chicken and a butcher’s knife.
[00:44:04] Like I’m giving you meal prepped meals ready for the week, right? So yeah, technically you could turn that chicken into what you need to. But but it’s a
[00:44:12] Charlie Madison: You better know how to de-feather.
[00:44:14] Zach Hammer: For you. I’m just giving it to you.
[00:44:17] Exactly. Otherwise it’s gonna be crunchy and, but yeah.
[00:44:22] Anyway, so yeah, again, if you’re interested in that. I’d be delighted to invite you if it doesn’t feel like it’s a good fit. Hopefully. I really do. I strive to make these shows, these episodes be jam-packed full of way more value than most things are ever gonna deliver completely for free.
[00:44:39] So you could learn a ton just by going back, listening through to these episodes, putting ’em into practice, applying it. But if you feel like you need that extra level of help, if you want that hand-holding to make it quicker, faster, better.
[00:44:51] Reach out to us RealEstateGrowthHackers.com/Contact.
[00:44:54] Let us know you’re interested in that AI Mastermind, and we’d love to get you in, get you involved, and join us on our journey [00:45:00] of implementing AI into the real estate world.
[00:45:02] There you go. All right. Until next time, we’ll catch you on the next episode of Real Estate Growth Hackers.
[00:45:08] Charlie Madison: Bye.
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Real Estate Growth Hackers Founder

Zach Hammer
Zach Hammer is the co-founder of Real Estate Growth Hackers. Over the last 36 months Zach and his team have managed ad budgets well over $100,000, generated over 25,000 real estate leads, and helped create over $50,000,0000 in business revenue for their clients. Zach is also a highly sought after speaker and consultant whose work has impacted some of the top Real Estate teams and brokerages across the country.