
Power Chat GPT Principles
Unlock the full potential of ChatGPT! Join hosts Zach Hammer and Charlie Madison on The Real Estate Growth Hackers as they dive into the key principles for getting the most out of large language models. Learn prompt engineering secrets and strategies to take your AI interactions to the next level. Whether you’re looking for high-quality outputs or consistent results, this episode has actionable tips to maximize your ChatGPT productivity.
Tune in as Zach and Charlie demystify techniques like mega prompts, memory management, focused threading, and more. Don’t miss these subtle but powerful tweaks that could mean exponential gains for your business. Discover the Power ChatGPT Principles – this week on The Real Estate Growth Hackers podcast!
Other subjects covered on the show
- Why understanding ChatGPT’s capabilities is key to getting desired results.
- Prompt engineering techniques to simulate experts and personas.
- Leveraging AI tools to simplify complex prompts.
- Custom instructions to provide helpful context.
- When interrupting ChatGPT can improve outcomes.
- Balancing focused conversations while allowing flexibility.
- The exponential benefits of fine-tuning prompts.
- Making small adjustments for big productivity gains.
- Using subtle principles, not busywork, for effectiveness.
- Ensuring ethical AI use for business applications.
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/
[00:00:00] Zach Hammer: welcome, welcome back to another episode of Real Estate Growth Hackers. I am Zach Hammer. And with me I have Charlie Madison, my co-host, the man, the myth, the legend, the incredibly well groomed with his recent haircut.
[00:00:12] How are you there, Charlie?
[00:00:14] Charlie Madison: Hey people. I’m back. I do feel so fresh and so clean.
[00:00:18] Zach Hammer: Yeah, there we go.
[00:00:19] Charlie Madison: It’s nice to be here.
[00:00:20] Zach Hammer: That’s right. on Today’s episode, we are gonna be talking about what I call some power ChatGPT principles. So, what am I talking about? Large language models have been out for a while. One of the most powerful ones that people maybe not the most powerful, one of the most leveraged is ChatGPT.
[00:00:36] That’s the one that people are most familiar with. And I’ve been using this tool for a long time now. I’ve been using it pretty much since it came out. I’ve been using large language models since before ChatGPT came out. And I’ve learned some of the key things that allow me to consistently get the results that I want.
[00:00:52] Whether it’s high quality outputs, whether it’s consistent outputs really and simply the way that I define it. Because these are some of the core principles [00:01:00] I’ve used or discovered that allow me to make the result happen that I want to have happen. They have me end up with, I am happy with what the AI is giving me back and for a number of reasons.
[00:01:12] That’s what we’re talking about, a little bit about why it’s important. Hopefully save you some time, effort, get you to a successful result with AI quicker. That’s key idea. Any any thoughts? Any feedback as we go into the actual principles themselves?
[00:01:22] Charlie Madison: You know what, once again whenever I have a question about prompts and how to use this I go to you because you’ve got your like magic prompt book feel like it’s held more secretly than Epstein’s Black Book. I haven’t gotten my hands on it yet. And probably more powerful.
[00:01:40] Zach Hammer: There you go. There you go.
[00:01:42] Charlie Madison: So, I am excited to see the inner workings of your ChatGPT Black Book.
[00:01:49] Zach Hammer: Sounds good. Sounds good. Cool. Yeah, let’s dive into it. Like first and foremost before I actually get into the principles, just this is part of the power of ChatGBT and large language models in general.[00:02:00] A lot of this stuff is fairly simple. Like you’re gonna hear it and it’s not gonna be like, oh, this is what I’ve been missing this whole time.
[00:02:07] And now not like it feels like a big amazing discovery. Like these things are they tend to be pretty simple, but it’s more about leveraging language, consistently understanding the technicalities for how these tools work. And when you do that, you’re able to more naturally get where you want to.
[00:02:23] And what’s the phrase? Little hinges, swing big doors. Like that’s the phrase, right? That’s a thing that, so, these are small principles that are very powerful in their results. Don’t expect to be looking for something that you’re like, ah, this is something I never could have figured out.
[00:02:39] You probably would. Honestly, over time, if you use these tools enough, you probably would. Or maybe after enough experimentation, you come to these. My goal is just to make that process shorter and simpler for everybody as we go through. Yeah, let’s dive into it.
[00:02:50] Let’s so the first one. It’s one that we’ve actually already done an episode on, so I won’t fully cover it. But the key principle is the mega prompt framework. And so the mega prompt [00:03:00] framework is my collection of ideas on prompt engineering and making sure of how to structure a prompt itself.
[00:03:07] Now, honestly I’ve been recently rewatching parks and recreation with my wife have you watched that show Charlie?
[00:03:13] Charlie Madison: I’ve seen parts of it. I have not gotten into it the way that I did the office.
[00:03:18] Zach Hammer: So, if in terms of a show, I highly recommend if you have it like the first season, I feel like takes a bit of effort to get past, it doesn’t hit as well as later seasons, but once you pass the first season, feel like that’s where it really hits its stride. Yeah Ron is a character, is probably one of my favorite characters in in TV history.
[00:03:35] Yeah definitely check it out. I think you’d like it. You and I actually probably have a mutual connection that might remind you of Ron. You might even know who I’m talking about once you make the connection. Anyway.
[00:03:43] The reason why I bring it up though, it is relevant to the mega prompts in the show. In a later season three or four, there’s a character. I think his name is Chris Traeger. It’s Rob Lowe is who plays the character. He is an auditor from the state of Indiana coming in to help make sure it get the city of [00:04:00] Pawnee back on track.
[00:04:01] Anyway, but he is a very quirky person, and one of the things that he does is when he’s asking for something, he asks for it in a very specific way. So he’s asking his assistant for a glass of water, and he says it like this. He says, I want a glass of water. I want it cold. I want it without ice.
[00:04:17] I want it in a mug or glass without any handles, and I want it quickly. And so he he asked for things like this, and I’m like, man, that is almost a perfect example of like how you’d leverage these things with AI. Is just by being very clear, very detailed, very explicit on what you want and and that’s really what the Mega Prompts framework is designed to do, is it teaches you how to be explicit.
[00:04:41] In the ways that matter using some of these, prompt engineering principles. For anybody who hasn’t seen that episode, the basic idea, I check out the episode for the full context, but the things that you’re gonna do in that mega prompt framework, you’re gonna ask ChatGPT to simulate a persona.
[00:04:54] So I’m gonna act as if I’m this person, like an expert Facebook copywriter, a master real [00:05:00] estate agent. I’m gonna act like a professional therapist, right? Whatever you need from ChatGPT at the time, you a ask it to act like that you’re gonna tell it, the task that you like it to do, the thing that you want to have happen.
[00:05:11] You wanna tell the goal what a successful outcome looks like as a result of that task, you’re gonna tell it steps to achieve that task. And the reason why you do that is when you get ChatGPT thinking step by step, you tend to get better results. So you lay out the steps first, I want you to analyze this document, then I want you to give me a summary.
[00:05:30] Then I want you to take that summary and turn it into the actual thing that I want from you. Those kinds of steps can actually give you a much better result. And then finally, what you’re gonna give ChatGPT is context and constraints. So that’s gonna be things like if you have character limits, if you need to give it a little bit of extra detail around why you’re doing this or what you need from it.
[00:05:48] It could also be things like tone style, output format, those kinds of things. There’s a lot of things that fit into context and constraints, but that’s where you give it the extra details to give you the flavor, the style, the formatting that you need it [00:06:00] to come back to you with.
[00:06:01] But that’s the key idea around the mega prompt framework. Any thoughts or questions around that one?
[00:06:07] Charlie Madison: We did cover that. I loved how we covered it in detail previously. I guess one of the questions is, if it’s hard for me to think through the steps, can I ask ChatGPT. What are some steps that you could use?
[00:06:22] Zach Hammer: Yes. Yes. And I actually have two frameworks. I have two prompts that I actually developed to make this easy for people. One is called prompt engineer GPT, and that would basically works with you where you get ChatGPT to act as a prompt engineer for you. So that. It asks you questions, you answer them, and it gives you back a mega prompt including all the steps to complete and all that sort of thing.
[00:06:46] Similarly I’ve got another one that allows you to take an already successful prompt and then throw this prompt in, at the end of it, and it’ll have, ChatGPT, take what you’ve already done and turn it into a mega prompt so that you could repeat that process without [00:07:00] all the back and forth that you probably had to do to get there in the first place.
[00:07:02] So yes you can absolutely leverage ChatGPT to get you those answers. Everything that’s in here, everything that I’m describing, you can always start foundationally with saying, Hey, here’s what I’m looking to do. Here’s what I’m looking to achieve. Can you ask me any questions that you need to ask me in order to get me to this outcome including steps, et cetera.
[00:07:20] Yeah, absolutely. You don’t have to provide all this upfront. You can leverage AI to make the process easier in the first place as well.
[00:07:26] Charlie Madison: I love that.
[00:07:28] Zach Hammer: Indeed. Indeed. Alright, moving on to principle number two. So principle number two is what I call the Dory principle. And the Dory principle all centers around the idea that ChatGPT does have memory issues at points, right? It does just Dory need to be refreshed, need to be reminded.
[00:07:47] And so it’s worthwhile. This may be a principle that actually goes away at some point. In terms of practicality as of right now there’s a specific window in ChatGPT, depending on the model you’re using of about [00:08:00] 4,000 tokens up to 16,000 tokens within ChatGPT. Although GPT-4 is capable of 32,000 tokens via the API, if I recall and what that means those tokens it’s not exactly this way, but it’s worth.
[00:08:14] For basic understanding, thinking of that as the number of words that it can understand at one given time. So approximately 4,000 words of context at a given time. That’s gonna include both any messages that you’ve sent it, as well as what you’re asking it to do, as well as what it’s trying to write and complete and continue further for you.
[00:08:32] So if you’ve got a really long chain. It may not remember the context of what you started with, and it’s only gonna be operating off of whatever was is within that window of How it’s operating. That 4,000 words, that 16,000 words, depending on which model you’re using.
[00:08:47] And this has impacts in terms of, is it keeping the context in mind of what you’re working on, what you’re looking for, that thing. And it’s worthwhile to be aware of that. A couple of ways that you could mitigate this and just work with [00:09:00] this principle recently, ChatGPT or OpenAI introduced the ability to include context as well as instructions that you could set via custom instructions via the settings.
[00:09:11] When you leverage those, those custom instructions stay with you through every message so that, you at least guarantee that some minimum level of continuation stays present no matter how long your message chain gets. So that’s one way to do it. The other way to do it is that if you need to remind ChatGPT of something that you’re doing or that you’ve worked on, you could always
[00:09:30] Get a summary copy and paste information, say, Hey, as a quick reminder, here’s what we’re working on now, here’s what I need you to do. And that could be another way to do it depending on how practically you’ve thought about it on the front end. But it’s worthwhile to remember. ChatGPT will forget things, right, there is a context that it will forget. And so it, you might need to remind it. You might to those various tools to keep that clear for you, does that make sense?
[00:09:56] Charlie Madison: Yeah. Have you used the context or the instructions at [00:10:00] all?
[00:10:01] Zach Hammer: I have, I find there’s character limits on those I think 1500 characters for each of them. And so that’s useful. I often have to leverage AI in order to shorten the kind of context that I’d want to include to fit into 1500 characters. But I do find that they’re useful. You could actually you could take like a mega prompts framework and throw that in as your instructions and possibly include context about who you are, what you’re looking for, who you’re trying to reach, your target market doing a shortened customer empathy map, those kinds of things into the context.
[00:10:30] And then that can make your results really clear, really quick. It really, I feel like there’s a, this is one of those things where there’s a little bit of an art to it and a little bit less of a of a science. I haven’t fully decided where I have found those custom instructions to be most useful or where I found them to possibly get in the way.
[00:10:49] ’cause sometimes you want ChatGPT to like modify with you sometimes, right? Sometimes you want it to learn from what you’ve most recently been doing rather than keep going back to what you started with. [00:11:00] And so there can be times where you actually don’t want the custom instructions but for the most part to keep the process simple.
[00:11:07] If you can keep one idea at a time and flow through a thread in that way, then those custom instructions are gonna help you a ton. I do find. I would recommend if you have ideas for what you wanna throw in the custom instructions, you probably wanna take that, take your ideas, write ’em down, send ’em through ChatGPT first, and ask it to shorten them and make ’em as concise as possible.
[00:11:27] Otherwise you might struggle to get the detail that you’d need into those custom instructions for them to be adequately helpful. But just ’cause those character limits. But but yes. So custom instructions can be helpful. I think they’re part of an overall mix of your strategy in dealing with the Dory principle for sure.
[00:11:42] So going into the next one, we’ve got what I call the interrupt principle. And I think this is one of those things I don’t know if other people do this. I know I do it a ton. There is a stop generating button in ChatGPT, where you could tell it. I want you to stop. [00:12:00] And when you realize, oh, I forgot to include something, or I see it’s going in a direction that I don’t actually want it to go, I will very often, as soon as I see that it’s going down a path that I don’t want it to go, I will stop it.
[00:12:13] And I will go back and edit my message to include any extra clarity, include any clarifying things to ensure that it’s coming back to me with the message that I want. And this is important for a couple of reasons. One don’t just sit there and wait for it to to do something. When you see that it’s clearly not doing what you want that’s a waste of your time.
[00:12:31] Get it to stop, go back and clarify more than anything. AI, these large language models are a mirror for your own clarity. So when you see them not giving you back what you want. That’s your answer, that you weren’t clear enough. Go back, fix your clarity, give it the context, ask it the question in a slightly different way so that it’s more accurate what you’re looking for and that’s gonna be really powerful for you.
[00:12:51] Did you have something that you wanted to say about that?
[00:12:54] Charlie Madison: No, just the noise has left around me so I can unmute now.
[00:12:57] Zach Hammer: Yeah. Gotcha. Gotcha. Yeah, good call. [00:13:00] So yeah so interrupt ChatGPT, press that stop generate button and go back and fix your context. And then further, the other reason why that matters actually goes back to our Dory principle.
[00:13:09] It’s a lot better to have all of the context window of ChatGPT be what you want it to do. Rather than a mixture of what you don’t want it to do, then you ask the question to clarify it like, you just want it to have tons of examples of perfection If you mix in imperfect or wrong stuff into it, it’s gonna start to confuse it as it tries to mix what it’s giving you in the output, right?
[00:13:36] Because it’s gonna see those examples. It’s not gonna destroy what you’re up to, but I’ve just found if I want it to be most reliable, having it see very clearly, this is exactly what I want, and continuing down a success path rather than trying to bring it back to success after it’s already been unsuccessful in the chat chain. So you fix it by going back and editing your message, getting it to give you back exactly what you’re looking for. [00:14:00] Then continuing on, rather than asking it, getting the wrong answer, then asking a new one to try and fix it. Instead go back and edit your original to try and get closer to the right message.
[00:14:10] Now, don’t have to do that every single, like every single of the time. I said that very specifically is a office reference there. But, yeah you don’t have to do that all the time, but I do find that it can help. So, that’s the idea. Any any thoughts around the interrupt principle and leveraging that stop generating button as well as editing your original prompts?
[00:14:31] Charlie Madison: Yeah, a lot of times, like when I hit the stop button, I like a lot of times it’ll say, which one of these are the best answer, or, that asks for feedback. And a lot of times I feel like when I click stop, like both options are just way off. So I’ve gotta just go back to the drawing board a little bit.
[00:14:49] Zach Hammer: Yeah. Yeah. And there’s actually two buttons. One of them is regenerate, which might be what you’re talking about. That’s when I typically see the two options come up. The stop generating typically it’s more like you put your [00:15:00] finger up, right?
[00:15:00] And it literally just stops it and its tracks. It’s like, all right, I’m done. And it just stops there. And so then that’s where you could go back and edit the message. We might be talking about the same thing. They do play with the UI a bit. So it’s possible that now sometimes when you click stop generating, it tries to regenerate something different as well.
[00:15:16] But yeah, either, either way it goes back to that same point of when you’re telling it to stop, when you’re needing to go back and fix something sometimes it’s close but you. Whatever reason you’re telling it to stop, whatever reason it’s off.
[00:15:28] It’s just better to fix that original message rather than trying to fix it through a follow-up question. So does that make sense?
[00:15:34] Charlie Madison: Yep. That makes sense.
[00:15:36] Zach Hammer: Perfect. Perfect. All right. And then our final principle, and by the way, these aren’t designed to be exhaustive. This is just a grab bag collection of some of the things that I found to make me more successful leveraging these AI models.
[00:15:46] So there might be more, there might be more things that you want to have in order to be successful with this. But the last one that I have for you today, it’s called The One Thing at a Time Principle. And I actually touched on this briefly. Basically the idea is you don’t want to have if you can avoid it, [00:16:00] you don’t want to mix use cases in one thread.
[00:16:02] You don’t wanna start by talking about getting help with social media posts. Then shift to getting advice on your taxes. Then shift to like asking it the best way to do a foot massage, right? Like you, you wanna use new threads for different topics in order to have your threads build up the most value as possible.
[00:16:20] Now, the place where you can potentially divert from this is, if you are building a chain of thought, that builds on itself, right? So, if I’m building a landing page and I get it to write the copy for the landing page, and then I want it to write emails about the same things that I put together for that landing page, and I wanted to get it to write social media posts to help promote the landing page as well.
[00:16:44] That kind of context can make sense to do all in one thread sometimes. So it can make sense to do that. It depends on if what you’re looking for is for very similar language to be pulled in and very similar thoughts to be pulled in. If you’re looking for that, then it’s a great way to kind of [00:17:00] give it all that context in one go fairly naturally as you’re building on an idea.
[00:17:04] But if you’re switching topics, switch threads as well in general. Otherwise, you’re just gonna get weird, confusing mixtures of like, ChatGPT is trying to decide. Okay, so I need to answer therapist questions plus, uh, copywriting plus art tips.
[00:17:23] I am so confused right now. My responses are going to be really weird. Anyway that’s what tends to happen. So, if you’re working on multiple things, start a new thread typically unless what you’re doing logically builds on the ideas that you started with foundationally.
[00:17:37] So that’s the key idea there. That’s the one thing at a time principle. Any thoughts on that one?
[00:17:44] Charlie Madison: Yeah, I’ve sometimes I’ve duplicated a thread or duplicated what I’ve got. ’cause I want to go down these two parallel paths and, instead of trying to jump, do one and then jump to the other, I just have the two separate kind of take it where it was left off.[00:18:00]
[00:18:00] And that seems and I started doing that I think ’cause it just felt cleaner because trying to get it to jump back and forth.
[00:18:06] Zach Hammer: Yeah, that makes sense. And actually, it’d be really cool if they added some way to do that in the UI, where there’s a split a conversation or fork a conversation all from the same thread. Just that you could see it, like, this is the parent thread.
[00:18:19] Anyway, I’d like that. That’d be cool. But yeah, that makes sense. And it’s definitely the same basic idea, right? Where sometimes you want it to have. So as a for instance, what I was just describing of building a landing page and then social media posts and emails. You might find in the same way that like, it’s better if you write the social media posts, the emails, the video template, the, all these other assets that you might do, you might find that works best when the most recent context is the landing page for each of them.
[00:18:48] And so that’s where you might like . Do what you just described of, you’re starting maybe four different conversations, but all of them start with the landing page, so that’s the most recent context for how it’s generating the other things.[00:19:00] So yeah, strategically that definitely makes sense.
[00:19:02] And it fits within the, for sure, within the frame of, what I’m talking about with that one thing at a time, really these are definitely more principles and guidelines and less rules. They’re the kinds of things that sometimes it can make sense to break ’em when, you know why and how.
[00:19:15] The key reason why I leverage these as principles is ’cause they really focus on understanding the technology itself and how it works, right? Understanding the memory window, understanding that when ChatGPT is giving you an answer. Part of what determines what answer comes is the context that you’re giving it.
[00:19:33] So understanding if you, if that context helps you, then great. Keep it as part of your conversation. If that context hurts you getting the result that you want, that’s when you need to start a new thread, or that’s when you need to leverage the strategy separately. Yeah, but as a general idea, that’s the concept there.
[00:19:50] So those principles again. Our four main principles for our power ChatGPT principles. We got leverage mega prompts. That’s that really fleshed out [00:20:00] concept. Thank Chris Traeger from Parks and Recreation. I want a glass of water. I want a cold. I want it without ice in it. , I want it quickly, I want it in a glass or mug without handles
[00:20:13] Anyway, so that’s our bigger prop framework. And then we got our Dory principle, just remembering that ChatGPT does have a limited memory window. So you wanna leverage that both to your advantage mitigate its problems as they come. You wanna make sure to interrupt ChatGPT.
[00:20:26] Don’t just let it do its own thing and don’t just continue down a bad chain of thought, trying to fix it. It’s off the better to go back and fix it where it went wrong in the beginning, and then the one thing at a time strategy for how you leverage your threads and messages and all of that.
[00:20:41] So those are the four principles that I got for you. Any thoughts or final questions or considerations on that Charlie?
[00:20:47] Charlie Madison: Listening to this, it made me think I was listening to Naval Revant the other day. And, uh, he was talking about knowledge workers and just the exponential leverage of it and he [00:21:00] said in a knowledge, why is this important? He said, when it comes to knowledge and the exponential nature of it, if you’ve got two people.
[00:21:08] One is right, 80% of the time, the other is right 99% of the time in the exponential world we’re in, this person is going to get paid tens of thousands of dollars more is going to be so much like the compounding interest. So just viewing this as really a superpower, uh, to get right and like even just a little bit better will compound on itself in these stages.
[00:21:38] Zach Hammer: Exactly and what’s powerful about this? Nothing that I just described it doesn’t really like, aside from maybe the mega prompt framework, which again could be mitigated by AI prompts that I’ve developed to make that easy. Aside from that one, most of these, they aren’t, they don’t increase the work, right?
[00:21:55] They don’t make this harder. It’s more about being more intelligent with how you’re using [00:22:00] the tool and how you’re leveraging it. So it’s not about you have to do all these extra steps. It’s more about here are the right ways to just subtly guide and zero in on an ideal outcome.
[00:22:09] And so it doesn’t even take much extra work if at all. It’s just more effective work.
[00:22:14] Charlie Madison: The guidance there is really subtle.
[00:22:18] Zach Hammer: It’s super subtle. . Indeed. Boo! Roasted So there you go. That is our power ChatGPT principles, . Thank you so much for coming out to another episode of Real Estate Growth Hackers. I’m Zach Hammer, founder real Estate Growth hackers. Feel free to check out what we are up to at RealEstateGrowthHackers.com. Everything that I talked about today.
[00:22:43] Any offers, anything that you might wanna check out, that’s where you’re gonna be able to find it. I keep having this voice in my ear saying that I need to turn some of these prompts into something that you guys could opt in for and and get access to that’ll be coming soon. So definitely check out Real Estate Growth Hackers as where you could get access to this information.
[00:22:58] If you want to dive in deeper[00:23:00] Charlie Madison, my co-host. Definitely check out what he is up to at Realtor Waiting List or Referrals While You Sleep. Realtor waiting list if you are a lender referrals while you sleep, if you’re a real estate professional. And he could point you to how you could be leveraging the strategy of being able to build a waiting list of realtors or get your friends, your family, the people that you know and trust to reach out to you on autopilot.
[00:23:22] Really powerful stuff and definitely worth checking out and again, RealtorWaitingList.com. And ReferralsWhileYouSleep.com where you check that out. There you go. Thanks again for coming on with me, Charlie. Wonderful as always. And we’ll see everybody on the next one.
[00:23:39] Charlie Madison: See you next time.
[00:23:43]
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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.