WEBVTT 00:00:00.000 --> 00:00:02.220 Have you noticed lately how much AI generated 00:00:02.220 --> 00:00:06.200 content can feel? Well, a bit generic. Oh, definitely. 00:00:06.379 --> 00:00:09.919 It's everywhere. Social media fees, blogs. It's 00:00:09.919 --> 00:00:12.179 often flooded with this kind of digital sameness. 00:00:12.179 --> 00:00:14.679 It all just sounds so alike. Yeah. Kind of like 00:00:14.679 --> 00:00:17.640 AI music. Exactly. Yeah. But what if there was, 00:00:17.679 --> 00:00:20.559 you know, a strategic advantage, a way to actually 00:00:20.559 --> 00:00:23.679 break free from that mediocrity and make AI outputs 00:00:23.679 --> 00:00:27.160 not just interesting, but like. actually profitable 00:00:27.160 --> 00:00:29.519 and scalable for your business. Right. Moving 00:00:29.519 --> 00:00:32.020 beyond just playing around with it. Welcome to 00:00:32.020 --> 00:00:35.140 the Deep Dive. Today, we're going to unpack that 00:00:35.140 --> 00:00:38.859 powerful technique, JSON prompting. Ah, yes. 00:00:39.299 --> 00:00:42.000 The good stuff. And this isn't just some clever 00:00:42.000 --> 00:00:44.520 trick, right? It feels like a fundamental shift 00:00:44.520 --> 00:00:46.299 in how we talk to these intelligent systems. 00:00:46.719 --> 00:00:48.799 for real business impact. That's exactly it. 00:00:48.840 --> 00:00:51.359 We'll dive into why AI results can be so inconsistent 00:00:51.359 --> 00:00:54.439 first off, then explain what JSON actually is 00:00:54.439 --> 00:00:56.679 and why it's so different from those traditional 00:00:56.679 --> 00:00:59.000 prompts most people are using. The conversational 00:00:59.000 --> 00:01:01.500 ones. Yeah. Then we'll get into some real -world 00:01:01.500 --> 00:01:03.500 business models that are actually powered by 00:01:03.500 --> 00:01:05.519 this structured approach. Oh, cool. We'll even 00:01:05.519 --> 00:01:08.540 walk you through the, let's say, the logic of 00:01:08.540 --> 00:01:10.980 creating your first JSON prompt. It's not as 00:01:10.980 --> 00:01:13.560 scary as it looks. Good to know. And then, crucially, 00:01:13.659 --> 00:01:17.099 how to test these things rigorously, how to improve 00:01:17.099 --> 00:01:19.180 them, and maybe the most important part, the 00:01:19.180 --> 00:01:21.620 pitfalls to avoid, the things that'll trip you 00:01:21.620 --> 00:01:25.370 up. Okay. So our mission today is really to shortcut 00:01:25.370 --> 00:01:27.890 your understanding of this method because it 00:01:27.890 --> 00:01:30.609 feels like a genuine game changer for building 00:01:30.609 --> 00:01:35.010 profitable, defensible businesses with AI. It 00:01:35.010 --> 00:01:38.109 really can be. OK, let's unpack this. So starting 00:01:38.109 --> 00:01:41.530 with this sea of sameness in AI content, it's 00:01:41.530 --> 00:01:43.349 a real challenge, isn't it? It really is. The 00:01:43.349 --> 00:01:45.969 Internet is just, well, brimming with low quality 00:01:45.969 --> 00:01:49.030 templated stuff, articles, videos, images. And 00:01:49.030 --> 00:01:51.269 so much of it looks and sounds the same. And 00:01:51.269 --> 00:01:52.719 you're saying it's. because millions are just 00:01:52.719 --> 00:01:54.939 using really basic prompting. Pretty much. They're 00:01:54.939 --> 00:01:57.299 using simple techniques. Our source material 00:01:57.299 --> 00:02:00.219 actually poses this fascinating question. When 00:02:00.219 --> 00:02:02.819 you prompt an AI, are you a poet or an engineer? 00:02:03.180 --> 00:02:06.640 Poet or engineer? So, okay, what's the difference? 00:02:06.780 --> 00:02:09.379 Well, the poet uses creative conversational language, 00:02:09.620 --> 00:02:11.979 you know, descriptive. Like talking to a person. 00:02:12.159 --> 00:02:15.080 Exactly. But the engineer, they're all about 00:02:15.080 --> 00:02:19.259 highly structured data -like formats. Very precise. 00:02:20.250 --> 00:02:22.849 Okay. And the source also mentioned a debater 00:02:22.849 --> 00:02:25.370 and a gambler. Yeah. The debater is that endless 00:02:25.370 --> 00:02:28.030 back and forth trying to negotiate a better result. 00:02:28.289 --> 00:02:30.250 I think we've all been there. Oh, yeah. And the 00:02:30.250 --> 00:02:33.550 gambler just types something in and, well, hopes 00:02:33.550 --> 00:02:36.289 for the best, crosses their fingers. Right. So 00:02:36.289 --> 00:02:38.669 how does this connect to the results? Well, the 00:02:38.669 --> 00:02:40.909 harsh reality is if you're still using a simple 00:02:40.909 --> 00:02:44.810 conversational prompt like, write an article 00:02:44.810 --> 00:02:47.300 about plants. Mm -hmm. You're competing with 00:02:47.300 --> 00:02:49.340 millions getting those same generic results. 00:02:49.500 --> 00:02:51.539 Your content just gets lost in the noise. It 00:02:51.539 --> 00:02:53.719 doesn't stand out. Because the instructions are 00:02:53.719 --> 00:02:56.139 too vague. Exactly. A prompt is just the instructions 00:02:56.139 --> 00:02:58.159 you give to an AI, right? If your instructions 00:02:58.159 --> 00:03:00.719 are vague, your output's going to be vague. Garbage 00:03:00.719 --> 00:03:02.639 in, well, maybe not garbage out, but generic 00:03:02.639 --> 00:03:07.979 out. So if these basic prompts lead to this undifferentiated 00:03:07.979 --> 00:03:11.659 kind of junk content, what's the fundamental 00:03:11.659 --> 00:03:15.400 shift JSON brings? JSON prompts provide a detailed 00:03:15.400 --> 00:03:18.860 blueprint for consistent, high -quality AI output. 00:03:19.120 --> 00:03:22.120 A blueprint. I like that. Okay, so what is this 00:03:22.120 --> 00:03:25.439 powerful format? Tell us about JSON. Okay, so 00:03:25.439 --> 00:03:29.180 JSON stands for JavaScript Object Notation. Fancy 00:03:29.180 --> 00:03:30.780 name, but it's basically just a standardized 00:03:30.780 --> 00:03:33.719 way to organize data. Really clean, really structured. 00:03:33.979 --> 00:03:35.680 So instead of talking to it conversationally. 00:03:35.919 --> 00:03:38.080 Right. Instead of those vague prompts, you give 00:03:38.080 --> 00:03:40.199 the AI this detailed blueprint. It tells the 00:03:40.199 --> 00:03:42.840 AI exactly what to do, how to structure the output 00:03:42.840 --> 00:03:45.319 and what rules it needs to follow. OK, got it. 00:03:45.360 --> 00:03:47.419 Think of it like this. The chef's analogy is 00:03:47.419 --> 00:03:49.520 great here. A regular prompt. That's like telling 00:03:49.520 --> 00:03:51.300 a chef, hey, cook me dinner. Right. Could be 00:03:51.300 --> 00:03:53.400 anything. Could be amazing. Could be. Well, not 00:03:53.400 --> 00:03:55.080 so amazing. It's a gamble, right? You gave no 00:03:55.080 --> 00:03:58.530 real instructions. But a JSON prompt? That's 00:03:58.530 --> 00:04:01.169 like giving the chef a super detailed recipe. 00:04:01.330 --> 00:04:03.610 Exact ingredients, precise measurements, step 00:04:03.610 --> 00:04:06.530 -by -step instructions. Okay, so the result isn't 00:04:06.530 --> 00:04:09.050 a guess. Not at all. It's predictable. It's high 00:04:09.050 --> 00:04:12.409 quality. Every single time. Or at least much 00:04:12.409 --> 00:04:14.750 closer to every time. And that's the core benefit 00:04:14.750 --> 00:04:18.230 then. consistency and reliability exactly because 00:04:18.230 --> 00:04:20.149 that predictability is what lets you take some 00:04:20.149 --> 00:04:23.209 fun little ai tool and turn it into an actual 00:04:23.209 --> 00:04:25.589 profitable business that can scale up precisely 00:04:25.589 --> 00:04:28.250 that's the leap so when you compare them side 00:04:28.250 --> 00:04:32.670 by side regular versus json prompting the differences 00:04:32.670 --> 00:04:36.610 sound pretty stark they really are regular prompts 00:04:36.610 --> 00:04:42.160 often generic inconsistent output json It's structured. 00:04:42.300 --> 00:04:44.720 It's predictable. And scalability. Almost impossible 00:04:44.720 --> 00:04:47.000 to replicate consistently with regular prompts. 00:04:47.199 --> 00:04:49.899 But JSON, it's designed for reuse. You just tweak 00:04:49.899 --> 00:04:51.639 the variables. Yeah, and for business applications. 00:04:52.100 --> 00:04:54.000 Regular prompts are kind of limited, maybe for 00:04:54.000 --> 00:04:56.300 one -off brainstorming. JSON, though, that's 00:04:56.300 --> 00:04:59.000 the engine for tools, for APIs, for automated 00:04:59.000 --> 00:05:01.240 workflows. It's built for business process. APIs. 00:05:01.439 --> 00:05:03.439 Just quickly, remind us what that is. Oh, right. 00:05:03.579 --> 00:05:06.339 An API application programming interface is just 00:05:06.339 --> 00:05:08.180 a way for different computer programs to talk 00:05:08.180 --> 00:05:09.899 to each other. It sends data back and forth in 00:05:09.899 --> 00:05:12.769 a structured way. Gotcha. And cost efficiency. 00:05:12.990 --> 00:05:14.829 You mentioned JSON can actually be more efficient. 00:05:15.069 --> 00:05:17.329 That seems counterintuitive. Yeah, it sounds 00:05:17.329 --> 00:05:19.889 like more work up front. But because the instructions 00:05:19.889 --> 00:05:22.410 are so compact and structured, you often use 00:05:22.410 --> 00:05:26.050 fewer resources, fewer tokens in AI terms over 00:05:26.050 --> 00:05:28.889 time. So those API calls can actually cost less, 00:05:29.029 --> 00:05:31.370 especially at scale. Interesting. So yeah, it 00:05:31.370 --> 00:05:33.509 all boils down to control. You stop being just 00:05:33.509 --> 00:05:35.589 a hopeful requester. Crossing your finger, yeah. 00:05:35.829 --> 00:05:38.069 And you become more like a system architect. 00:05:38.149 --> 00:05:41.189 You're designing the output. OK, so why is this 00:05:41.189 --> 00:05:44.310 structured approach, this control, specifically 00:05:44.310 --> 00:05:47.910 valuable in modern business like right now? Because 00:05:47.910 --> 00:05:51.290 modern business runs on structured data. JSON 00:05:51.290 --> 00:05:54.290 lets AI speak that language. That's a huge point. 00:05:54.449 --> 00:05:56.870 Everything is data. It really is. It's like the 00:05:56.870 --> 00:05:59.449 universal language for computers now. Everything 00:05:59.449 --> 00:06:02.029 we interact with, words, images, customer behavior, 00:06:02.269 --> 00:06:05.410 even DNA, it can all be represented as data. 00:06:05.920 --> 00:06:08.860 And to truly work effectively with AI, you have 00:06:08.860 --> 00:06:11.300 to learn to speak this language. It's not optional 00:06:11.300 --> 00:06:13.259 anymore, not if you want serious results. So 00:06:13.259 --> 00:06:15.120 the big tech companies get this already. Oh, 00:06:15.120 --> 00:06:18.060 absolutely. Look at Google, Microsoft, Netflix, 00:06:18.420 --> 00:06:22.160 Amazon. They're betting multi -billions on structured 00:06:22.160 --> 00:06:24.699 data. They know it's the most valuable asset 00:06:24.699 --> 00:06:26.819 on the planet, arguably. How are they using it? 00:06:27.000 --> 00:06:28.819 Well, Netflix uses your viewing data, structured 00:06:28.819 --> 00:06:31.779 data, to decide which shows to make. Amazon uses 00:06:31.779 --> 00:06:33.740 purchasing data, structured data for recommendations. 00:06:34.120 --> 00:06:36.699 They've built entire empires on refining and 00:06:36.699 --> 00:06:39.480 acting on structured data. So data is the new 00:06:39.480 --> 00:06:43.160 oil, but structured data is the refined fuel. 00:06:43.399 --> 00:06:45.220 Is that a fair way to put it? That's a great 00:06:45.220 --> 00:06:48.120 analogy. Yeah. And it suggests that maybe most 00:06:48.120 --> 00:06:50.379 businesses are still kind of stuck dealing with 00:06:50.379 --> 00:06:53.279 the crude oil stage when it comes to AI. So where's 00:06:53.279 --> 00:06:56.019 the biggest bottleneck then in that refinement 00:06:56.019 --> 00:06:58.759 process, getting from crude idea to refined AI 00:06:58.759 --> 00:07:01.730 fuel? Honestly? Often it's the human element. 00:07:01.810 --> 00:07:04.730 It's taking those messy, unstructured human ideas 00:07:04.730 --> 00:07:07.569 and translating them into clean, calculable data 00:07:07.569 --> 00:07:09.709 that an AI can actually work with effectively. 00:07:10.129 --> 00:07:12.050 It's a translation task. It really is. And it 00:07:12.050 --> 00:07:14.310 becomes like a professional reflex. One expert 00:07:14.310 --> 00:07:16.810 we looked at said, if I ever used AI to get a 00:07:16.810 --> 00:07:18.329 really good result, I would take that result 00:07:18.329 --> 00:07:20.449 and turn it into a JSON prompt so I can get that 00:07:20.449 --> 00:07:23.970 again. Ah, so capture the success, systematize 00:07:23.970 --> 00:07:26.730 it. Exactly. Don't just get lucky once. Engineer 00:07:26.730 --> 00:07:29.110 it so you can repeat it. Okay, this makes sense 00:07:29.110 --> 00:07:32.980 conceptually. But beyond the theory, what specific 00:07:32.980 --> 00:07:35.860 business models are actually using this JSON 00:07:35.860 --> 00:07:38.500 approach right now? Businesses are using JSON 00:07:38.500 --> 00:07:41.779 for automated content. webinars, custom tools, 00:07:41.899 --> 00:07:44.740 and boosting efficiency. Lots of areas. Right. 00:07:44.800 --> 00:07:46.720 So this isn't just theoretical pie in the sky 00:07:46.720 --> 00:07:48.980 stuff. It's happening now. Absolutely. It's very 00:07:48.980 --> 00:07:52.360 real. Think about, say, content as a service. 00:07:52.620 --> 00:07:55.279 Okay. Businesses are desperate for high quality, 00:07:55.420 --> 00:07:58.519 consistent content. They know they need it, but 00:07:58.519 --> 00:08:00.920 often they don't know how to prompt effectively 00:08:00.920 --> 00:08:03.029 to get it. So there's an opportunity there. Huge 00:08:03.029 --> 00:08:05.889 opportunity. You can step in as their outsourced 00:08:05.889 --> 00:08:08.769 AI content strategist. You could sell libraries 00:08:08.769 --> 00:08:12.009 of pre -built, battle -tested JSON prompts for 00:08:12.009 --> 00:08:14.509 things like blog articles, social media posts, 00:08:14.689 --> 00:08:17.290 email sequences. Or you could offer done -for 00:08:17.290 --> 00:08:19.689 -you services, where you use your internal JSON 00:08:19.689 --> 00:08:22.189 systems to generate the content for them, or 00:08:22.189 --> 00:08:24.250 even license your systems out to agencies. So 00:08:24.250 --> 00:08:26.310 you're selling the structured process, not just 00:08:26.310 --> 00:08:28.949 the end result. Exactly. And it goes beyond just 00:08:28.949 --> 00:08:31.709 blog posts. We saw an example of an entrepreneur. 00:08:32.110 --> 00:08:36.210 who had this webinar script, generated $14 ,000 00:08:36.210 --> 00:08:39.200 in revenue in just a few hours. pretty good well 00:08:39.200 --> 00:08:42.139 that so what they did was deconstruct its winning 00:08:42.139 --> 00:08:44.919 formula the hooks the structure the calls to 00:08:44.919 --> 00:08:48.460 action and turned it into a detailed json prompt 00:08:48.460 --> 00:08:52.799 template wow now they can generate new high converting 00:08:52.799 --> 00:08:55.059 webinar scripts basically on demand just change 00:08:55.059 --> 00:08:57.399 the variables for the product the audience boom 00:08:57.399 --> 00:08:59.320 new script that's that replication advantage 00:08:59.320 --> 00:09:01.519 you mentioned yeah extract the success make it 00:09:01.519 --> 00:09:03.860 repeatable that's it scale the process with perfect 00:09:03.860 --> 00:09:06.980 consistency what else another huge area is building 00:09:06.980 --> 00:09:10.129 and selling a API tools, sometimes called Microsoft 00:09:10.129 --> 00:09:13.620 little software as a service tools. There's massive 00:09:13.620 --> 00:09:16.299 search volume online for simple AI tools. Think 00:09:16.299 --> 00:09:19.440 AI text generator, AI logo generator, AI summary 00:09:19.440 --> 00:09:22.120 tool. People will happily pay a small fee for 00:09:22.120 --> 00:09:24.440 a tool that solves one specific problem really 00:09:24.440 --> 00:09:26.399 well. And you could build these with JSON prompts. 00:09:26.759 --> 00:09:29.179 Yeah, you can build them yourself, often using 00:09:29.179 --> 00:09:31.419 no -code or low -code platforms like Bubble or 00:09:31.419 --> 00:09:34.120 SoftR. You don't necessarily need to be a hardcore 00:09:34.120 --> 00:09:36.879 coder. How did that work? Well, first, you kind 00:09:36.879 --> 00:09:39.820 of deconstruct success. Analyze the best existing 00:09:39.820 --> 00:09:42.299 tools for a specific task. What makes them... 00:09:42.320 --> 00:09:45.919 work okay reverse engineer right then you engineer 00:09:45.919 --> 00:09:48.820 a master json prompt that replicates those results 00:09:48.820 --> 00:09:51.539 maybe adds your own improvements this prompt 00:09:51.539 --> 00:09:54.659 is the brain of your tool got it finally you 00:09:54.659 --> 00:09:58.299 build a simple interface a web page maybe this 00:09:58.299 --> 00:10:01.019 interface takes the user's input like their company 00:10:01.019 --> 00:10:03.720 name or the text they want summarized it feeds 00:10:03.720 --> 00:10:06.480 that input into your master prompt via an api 00:10:06.480 --> 00:10:09.139 call to an ai like chat gpt and the ai sends 00:10:09.139 --> 00:10:11.690 back the structured result exactly yeah User 00:10:11.690 --> 00:10:13.690 gets their logo idea, their summary, whatever 00:10:13.690 --> 00:10:15.850 it is. Simple problem solved. That's clever. 00:10:16.029 --> 00:10:18.590 And then there's business automation. Yeah, high 00:10:18.590 --> 00:10:20.750 -value business automation. Think about small 00:10:20.750 --> 00:10:23.570 and medium businesses. Many are just drowning 00:10:23.570 --> 00:10:26.509 in repetitive manual tasks. Sorting customer 00:10:26.509 --> 00:10:29.909 emails, summarizing long reports, drafting standard 00:10:29.909 --> 00:10:32.700 documents. Things that take up hours but don't 00:10:32.700 --> 00:10:34.480 necessarily require deep strategic thinking. 00:10:34.679 --> 00:10:37.000 They're actively looking for ways to automate 00:10:37.000 --> 00:10:39.279 this stuff. So you can step in there too. Definitely. 00:10:39.399 --> 00:10:41.200 You can become a business process automation 00:10:41.200 --> 00:10:44.960 consultant, a BPA consultant. Your core offering 00:10:44.960 --> 00:10:48.500 is building custom JSON prompt systems tailored 00:10:48.500 --> 00:10:50.779 specifically to their workflows. Can you give 00:10:50.779 --> 00:10:54.460 an example? Sure. Imagine a law firm. They get 00:10:54.460 --> 00:10:57.279 hundreds of emails a day. Someone has to read 00:10:57.279 --> 00:10:59.500 each one, figure out who it's from, what it's 00:10:59.500 --> 00:11:02.000 about, which case it relates to, how urgent it 00:11:02.000 --> 00:11:04.820 is. It takes hours. Yeah, I can see that. You 00:11:04.820 --> 00:11:07.559 could build a system with a JSON prompt. It analyzes 00:11:07.559 --> 00:11:10.789 each incoming email. extracts the key info client 00:11:10.789 --> 00:11:14.529 name case type urgency level maybe even summarizes 00:11:14.529 --> 00:11:16.889 the main point and categorizes it automatically 00:11:16.889 --> 00:11:19.149 that would save a ton of time dozens of hours 00:11:19.149 --> 00:11:21.429 per week easily maybe more so you're not just 00:11:21.429 --> 00:11:23.529 selling you know content generation you're selling 00:11:23.529 --> 00:11:26.750 raw efficiency and that's incredibly valuable 00:11:26.750 --> 00:11:30.559 to businesses whoa Imagine scaling that law firm 00:11:30.559 --> 00:11:33.779 example, like automating thousands of hours across 00:11:33.779 --> 00:11:36.860 an entire industry. The sheer volume of manual 00:11:36.860 --> 00:11:38.700 work out there that could potentially be transformed. 00:11:38.940 --> 00:11:41.139 Yeah. It's kind of immense when you think about 00:11:41.139 --> 00:11:45.039 it. It really is. The potential is huge. So beyond 00:11:45.039 --> 00:11:47.240 just getting better output, what do you think 00:11:47.240 --> 00:11:49.860 is the deeper cognitive shift here? What really 00:11:49.860 --> 00:11:53.220 changes for someone who... embraces this. Got 00:11:53.220 --> 00:11:55.220 to be about thinking differently, right? Thinking 00:11:55.220 --> 00:11:57.720 in terms of data and systems, almost like the 00:11:57.720 --> 00:12:00.659 AI itself does. Exactly. That's the core of it. 00:12:00.740 --> 00:12:02.320 You've really hit on the most important transformation 00:12:02.320 --> 00:12:06.879 there. It's mental. Mastering JSON, it forces 00:12:06.879 --> 00:12:08.899 you to stop thinking in these vague sentences 00:12:08.899 --> 00:12:11.659 and start thinking the way an AI actually thinks, 00:12:11.679 --> 00:12:14.559 or at least processes information in terms of 00:12:14.559 --> 00:12:17.080 data rules and systems. And this systematic approach, 00:12:17.139 --> 00:12:19.159 it gives your business or your projects like. 00:12:19.470 --> 00:12:21.850 Five superpowers. Ooh, superpowers. Okay, what 00:12:21.850 --> 00:12:24.210 are they? All right, first, consistency. Your 00:12:24.210 --> 00:12:26.750 results become predictable, reliable, no more 00:12:26.750 --> 00:12:31.190 guesswork. Second, scalability. Your systems 00:12:31.190 --> 00:12:33.850 can work at pretty much any volume without a 00:12:33.850 --> 00:12:36.990 drop in quality. One request or a million, the 00:12:36.990 --> 00:12:39.909 process holds. Crucial for growth. Third, efficiency. 00:12:40.830 --> 00:12:43.129 You generally get faster results and, like we 00:12:43.129 --> 00:12:45.370 said, potentially lower API costs over the long 00:12:45.370 --> 00:12:49.000 run. Good. Fourth, professionalism. Your output 00:12:49.000 --> 00:12:51.139 can be designed to automatically match industry 00:12:51.139 --> 00:12:54.279 best practices or specific formatting requirements. 00:12:54.460 --> 00:12:58.289 Okay, and fifth? A competitive moat. Your unique, 00:12:58.429 --> 00:13:01.029 systematic approach, the specific way you've 00:13:01.029 --> 00:13:03.370 structured your JSON prompts, that's incredibly 00:13:03.370 --> 00:13:05.990 difficult for competitors to just copy. It becomes 00:13:05.990 --> 00:13:08.629 a real asset. A defensible advantage. Precisely. 00:13:08.629 --> 00:13:10.870 An expert in the field really underscored this, 00:13:10.950 --> 00:13:14.009 saying, in the age of AI, you've got to understand 00:13:14.009 --> 00:13:15.570 this because that's going to give you the leg 00:13:15.570 --> 00:13:18.250 up. It's about differentiation. So the real upgrade 00:13:18.250 --> 00:13:20.750 isn't just the prompt. It's thinking like a computer. 00:13:20.950 --> 00:13:23.710 It upgrades your brain's operating system, maybe. 00:13:24.440 --> 00:13:26.179 I like that. Yeah, it moves you from traditional 00:13:26.179 --> 00:13:28.879 kind of vague thinking to structured systems 00:13:28.879 --> 00:13:30.980 thinking. So traditional thinking is like, write 00:13:30.980 --> 00:13:33.039 me something good about elephants. Okay, very 00:13:33.039 --> 00:13:35.700 poet -like. Right. But systems thinking is more 00:13:35.700 --> 00:13:38.779 like, generate content about elephants that follows 00:13:38.779 --> 00:13:41.360 this proven engagement pattern, includes these 00:13:41.360 --> 00:13:43.899 specific structural elements like a hook and 00:13:43.899 --> 00:13:47.700 key facts, uses this tone, and is designed to 00:13:47.700 --> 00:13:49.879 achieve this measurable outcome, like click -through 00:13:49.879 --> 00:13:52.990 rate. Much more engineer -like. Much more specific. 00:13:53.190 --> 00:13:55.649 Exactly. So this isn't just about writing better 00:13:55.649 --> 00:13:58.029 prompts for ChatGPT. It's about learning how 00:13:58.029 --> 00:14:01.269 to communicate effectively with any complex automated 00:14:01.269 --> 00:14:03.830 system. It's a fundamental skill for the future. 00:14:04.029 --> 00:14:07.590 This all sounds incredibly powerful. But for 00:14:07.590 --> 00:14:10.230 someone new to this, what's the most common mental 00:14:10.230 --> 00:14:13.009 block or maybe the biggest hurdle they'll face 00:14:13.009 --> 00:14:15.070 when trying to actually adopt this new way of 00:14:15.070 --> 00:14:17.759 thinking? I think many people get stuck seeing 00:14:17.759 --> 00:14:20.340 JSON and thinking it looks like code, like programming, 00:14:20.559 --> 00:14:22.700 and that can be intimidating. The real challenge 00:14:22.700 --> 00:14:24.840 is shifting your view, seeing it not as code, 00:14:24.879 --> 00:14:26.919 but just as highly organized instructions, like 00:14:26.919 --> 00:14:30.220 a very, very detailed recipe. Okay, just structured 00:14:30.220 --> 00:14:32.360 instructions. I know JSON can look a bit intimidating 00:14:32.360 --> 00:14:34.100 at first glance, you know, with all those curly 00:14:34.100 --> 00:14:36.919 braces and commas and quotes. Yeah, the syntax 00:14:36.919 --> 00:14:39.039 can seem weird. But you're right. If you think 00:14:39.039 --> 00:14:40.919 of it as just organizing your instructions clearly, 00:14:41.179 --> 00:14:44.480 maybe it's less daunting. Exactly. Let's maybe 00:14:44.480 --> 00:14:47.299 walk through the logic of creating one, say for 00:14:47.299 --> 00:14:49.360 writing a high converting product description. 00:14:49.620 --> 00:14:52.179 How would you approach that systematically? Okay. 00:14:52.259 --> 00:14:55.279 Good example. All right. Step one, define your 00:14:55.279 --> 00:14:58.440 goal clearly and be specific. Don't just say, 00:14:58.519 --> 00:15:01.320 write a description. Right. Too vague. A much 00:15:01.320 --> 00:15:05.110 better goal is something like. Generate a persuasive 00:15:05.110 --> 00:15:07.190 product description that must include a catchy 00:15:07.190 --> 00:15:10.570 title, a list of 3 -5 key features, a list of 00:15:10.570 --> 00:15:13.429 2 -3 emotional benefits, and a strong call to 00:15:13.429 --> 00:15:15.990 action. See the difference. Yeah, much clearer. 00:15:16.190 --> 00:15:19.450 It has requirements. Exactly. Step 2. Break your 00:15:19.450 --> 00:15:22.149 goal into logical sections. Think like that computer 00:15:22.149 --> 00:15:24.509 again. What are the core data fields you need 00:15:24.509 --> 00:15:27.789 the AI to understand or fill in? Okay, so for 00:15:27.789 --> 00:15:30.289 the product description. Maybe the product name 00:15:30.289 --> 00:15:32.990 itself. Yep. Product name. What else? Who it's 00:15:32.990 --> 00:15:35.990 for. The target audience. Good one. Target audience. 00:15:36.070 --> 00:15:38.250 We mentioned key features. Maybe specify that 00:15:38.250 --> 00:15:40.169 as a list. Right. So key features as a list. 00:15:40.330 --> 00:15:43.269 And emotional benefits also as a list. Perfect. 00:15:43.309 --> 00:15:46.370 Maybe tone of voice. Like playful or professional. 00:15:47.149 --> 00:15:50.049 And definitely the culture action. Okay. So these 00:15:50.049 --> 00:15:52.129 become like categories for our instruction. Exactly 00:15:52.129 --> 00:15:55.559 right. Then step three. You actually write your 00:15:55.559 --> 00:15:58.379 JSON structure using these categories. You format 00:15:58.379 --> 00:16:01.519 them using keys. That's the label, like product 00:16:01.519 --> 00:16:04.840 name in quotes and values, which are your specific 00:16:04.840 --> 00:16:07.480 instructions or the variables the user will input. 00:16:07.679 --> 00:16:09.879 So it looks kind of like a structured form. That's 00:16:09.879 --> 00:16:11.659 a great way to think about it. You're telling 00:16:11.659 --> 00:16:13.840 the AI, here's a field for the product name. 00:16:14.000 --> 00:16:16.919 Fill this in. Here's a list for features. Populate 00:16:16.919 --> 00:16:19.460 this. Here's another list for benefits. Use this 00:16:19.460 --> 00:16:22.159 specific tone. You're building a very precise 00:16:22.159 --> 00:16:24.259 questionnaire for the AI. Okay, that makes sense. 00:16:24.399 --> 00:16:26.980 What's next? Step four, you have to instruct 00:16:26.980 --> 00:16:29.019 the AI to use your structure. You've got to be 00:16:29.019 --> 00:16:30.870 expert. explicit about it. You can't just assume 00:16:30.870 --> 00:16:33.529 it knows. Nope. You literally tell the AI something 00:16:33.529 --> 00:16:36.149 like, based on the following JSON object containing 00:16:36.149 --> 00:16:38.509 product details and instructions, generate the 00:16:38.509 --> 00:16:41.049 product description. Your response must also 00:16:41.049 --> 00:16:43.950 be in a valid JSON format using a single key 00:16:43.950 --> 00:16:46.409 called product description, which contains the 00:16:46.409 --> 00:16:48.769 full text. Ah, so you tell it how to respond 00:16:48.769 --> 00:16:51.169 too. Why is that important? It ensures the AI 00:16:51.169 --> 00:16:53.450 uses your input correctly and gives you clean, 00:16:53.649 --> 00:16:55.929 predictable output that another system like your 00:16:55.929 --> 00:16:58.909 website can easily understand and use. No weird 00:16:58.909 --> 00:17:01.470 formatting issues. Got it. Structured input, 00:17:01.570 --> 00:17:04.490 structured output. Bingo. And finally, step five. 00:17:04.650 --> 00:17:07.950 Reuse, repurpose, and build your library. Once 00:17:07.950 --> 00:17:10.930 you have a JSON prompt that works well... Don't 00:17:10.930 --> 00:17:13.710 just use it once. Exactly. It's a reusable asset. 00:17:14.659 --> 00:17:16.900 Swap out the variable's product name, features 00:17:16.900 --> 00:17:19.059 for different products, tweak the tone of voice 00:17:19.059 --> 00:17:21.039 for different marketing campaigns or niches. 00:17:21.240 --> 00:17:23.660 So you capture the successful structure. Capture 00:17:23.660 --> 00:17:26.619 it in JSON and reuse it. Build up that library 00:17:26.619 --> 00:17:28.880 of proven prompts. You know, I have to admit, 00:17:29.019 --> 00:17:31.599 I still wrestle with prompt drift myself sometimes, 00:17:31.839 --> 00:17:34.759 even using JSON. Oh, yeah. What's prompt drift 00:17:34.759 --> 00:17:37.119 again? That's when, you know, an AI's output 00:17:37.119 --> 00:17:40.619 kind of... subtly shifts or deviates over time, 00:17:40.859 --> 00:17:42.880 even if you're using the exact same prompt you 00:17:42.880 --> 00:17:45.980 used last week. The model updates, things change. 00:17:46.220 --> 00:17:48.240 Right. It's not always perfectly static. Yeah. 00:17:48.319 --> 00:17:51.039 So keeping it consistent, even with JSON, it's 00:17:51.039 --> 00:17:53.319 definitely a continuous process of monitoring 00:17:53.319 --> 00:17:55.599 and tweaking. It's not always set and forget. 00:17:55.839 --> 00:17:57.980 That's a really good point. Maintenance is key. 00:17:58.200 --> 00:18:00.680 Can you give us maybe a full concrete example 00:18:00.680 --> 00:18:03.279 of a more complex system built this way, something 00:18:03.279 --> 00:18:06.759 beyond just a product description? Absolutely. 00:18:06.960 --> 00:18:09.819 Let's think about that AI logo generator system 00:18:09.819 --> 00:18:12.559 we mentioned earlier, how you could reverse engineer 00:18:12.559 --> 00:18:15.259 design principles to build it. Okay, the logo 00:18:15.259 --> 00:18:17.359 generator. Let's dive into that. Okay, so the 00:18:17.359 --> 00:18:20.019 core idea behind an AI logo generator system 00:18:20.019 --> 00:18:22.640 built with JSON is kind of like reverse engineering 00:18:22.640 --> 00:18:25.619 a masterpiece painting. How so? Well, you first 00:18:25.619 --> 00:18:28.099 study the techniques of the great masters, in 00:18:28.099 --> 00:18:30.740 this case, great logo designers. You figure out 00:18:30.740 --> 00:18:32.859 why their work is effective. Then you create 00:18:32.859 --> 00:18:34.859 detailed instructions based on those techniques. 00:18:35.160 --> 00:18:38.119 And finally, you build a robot painter, your 00:18:38.119 --> 00:18:40.980 AI system, to execute those instructions. Okay, 00:18:41.019 --> 00:18:43.440 so step one is studying the masters. Yeah. Research. 00:18:43.920 --> 00:18:47.099 Exactly. So step one is the research phase. Think 00:18:47.099 --> 00:18:49.700 of it as your art history phase for logos. You 00:18:49.700 --> 00:18:52.079 start with a specific research prompt. A prompt 00:18:52.079 --> 00:18:54.640 just for research. Yeah. You'd ask the AI to 00:18:54.640 --> 00:18:58.039 analyze, say, 100 successful logos across different 00:18:58.039 --> 00:19:00.700 industries, look for common patterns and color 00:19:00.700 --> 00:19:03.700 schemes, font styles, shape simplicity, how well 00:19:03.700 --> 00:19:06.460 they fit the industry, memorability. And you'd 00:19:06.460 --> 00:19:08.599 ask it to output those key patterns in a structured 00:19:08.599 --> 00:19:11.940 way, maybe even JSON. So you're using AI to figure 00:19:11.940 --> 00:19:14.559 out what makes a great logo in general. Precisely. 00:19:14.559 --> 00:19:16.900 You're extracting the design principles. Okay, 00:19:16.960 --> 00:19:19.559 then step two must be building the actual generator 00:19:19.559 --> 00:19:23.359 prompt. Yep. Step two, the system creation. Writing 00:19:23.359 --> 00:19:25.400 the master prompt, you take all those insights 00:19:25.400 --> 00:19:27.779 from your research phase, those principles, and 00:19:27.779 --> 00:19:29.599 you use them to build a really sophisticated 00:19:29.599 --> 00:19:32.500 logo generator JSON prompt. This prompt would 00:19:32.500 --> 00:19:34.839 take simple variables from the user, like their 00:19:34.839 --> 00:19:37.319 company name and industry, but it would apply 00:19:37.319 --> 00:19:40.000 fixed rules based on your research. Things like 00:19:40.000 --> 00:19:42.140 appropriate color psychology for that industry, 00:19:42.440 --> 00:19:45.400 guidelines on font pairing, principles of shape 00:19:45.400 --> 00:19:48.180 and simplicity. So you'd find keys like task, 00:19:48.359 --> 00:19:51.400 company name, industry, maybe preferred style. 00:19:51.980 --> 00:19:54.339 And then crucially, you'd include sections like 00:19:54.339 --> 00:19:56.420 color psychology rules and design principles 00:19:56.420 --> 00:19:58.799 that contain the distilled wisdom from your research 00:19:58.799 --> 00:20:02.079 phase. The AI must follow these rules. So the 00:20:02.079 --> 00:20:04.759 user gives simple input, but the prompt applies 00:20:04.759 --> 00:20:08.099 complex expert rules. That's the magic. And then 00:20:08.099 --> 00:20:11.329 step three is the implementation. building the 00:20:11.329 --> 00:20:14.269 actual robot painter. The tool itself. Right. 00:20:14.349 --> 00:20:16.930 You create a simple web tool, maybe using bubble 00:20:16.930 --> 00:20:19.230 or software like we mentioned. Just a couple 00:20:19.230 --> 00:20:22.450 of input fields. Company name, industry. Okay. 00:20:22.529 --> 00:20:25.579 When the user hits generate. The tool sends your 00:20:25.579 --> 00:20:28.119 master JSON prompt filled with their input and 00:20:28.119 --> 00:20:31.440 your embedded rules to an AI API like ChatGPT. 00:20:31.700 --> 00:20:34.160 And the API sends back? It sends back maybe several 00:20:34.160 --> 00:20:36.960 professional looking logo concepts, potentially 00:20:36.960 --> 00:20:39.559 a design rationale explaining why certain choices 00:20:39.559 --> 00:20:41.460 were made based on the rules, and maybe even 00:20:41.460 --> 00:20:44.440 usage guidelines for the new brand. All structured, 00:20:44.599 --> 00:20:47.059 ready to display. Wow, that's a pretty sophisticated 00:20:47.059 --> 00:20:49.359 system built on that structured prompting idea. 00:20:49.680 --> 00:20:51.960 But how do you make sure these powerful systems 00:20:51.960 --> 00:20:54.339 actually work reliably? You know, that they don't 00:20:54.339 --> 00:20:57.440 just break or give weird results sometimes. The 00:20:57.440 --> 00:20:59.279 million dollar question. You have to battle test 00:20:59.279 --> 00:21:02.619 them rigorously, consistently. And then you keep 00:21:02.619 --> 00:21:04.880 refining them. It never really stops. Battle 00:21:04.880 --> 00:21:07.279 test. I like that. So it's not enough just to 00:21:07.279 --> 00:21:10.059 write the prompt. Absolutely not. A prompt that 00:21:10.059 --> 00:21:12.200 hasn't been rigorously tested. It's just a guess. 00:21:12.319 --> 00:21:15.700 It's a hypothesis. To build a reliable AI system, 00:21:15.960 --> 00:21:19.000 you must battle test your JSON prompts. Find 00:21:19.000 --> 00:21:21.660 their breaking points before your users do. Okay. 00:21:22.000 --> 00:21:23.960 How do you do that? Is there a process? Yeah, 00:21:24.000 --> 00:21:26.779 think of it as a continuous two -stage process. 00:21:26.940 --> 00:21:29.019 There's the initial stress testing and then the 00:21:29.019 --> 00:21:31.079 ongoing improvement. Okay, let's start with the 00:21:31.079 --> 00:21:33.720 initial stress test. Right. Our source suggests 00:21:33.720 --> 00:21:36.799 a neat three -part framework for that. First 00:21:36.799 --> 00:21:39.019 is the consistency test. Makes sense. Run the 00:21:39.019 --> 00:21:41.720 exact same prompt, same variables, at least 10 00:21:41.720 --> 00:21:44.970 times. Measure the variation in the output. Are 00:21:44.970 --> 00:21:47.369 the results wildly different each time? If so, 00:21:47.529 --> 00:21:49.450 your prompt isn't constrained enough, you need 00:21:49.450 --> 00:21:52.369 to refine it, add more rules or structure, until 00:21:52.369 --> 00:21:54.930 the output is reliably consistent. Okay, test 00:21:54.930 --> 00:21:58.130 for consistency. What's second? Second. The performance 00:21:58.130 --> 00:22:01.549 test. This is where you A -B test. Pit your shiny 00:22:01.549 --> 00:22:04.529 new JSON prompt against a traditional conversational 00:22:04.529 --> 00:22:07.130 one for the same task. Ah, a direct comparison. 00:22:07.490 --> 00:22:09.509 Yeah. And measure the real -world difference. 00:22:09.789 --> 00:22:12.430 Does the content generated by your JSON prompt 00:22:12.430 --> 00:22:14.630 get higher engagement, better click -through 00:22:14.630 --> 00:22:16.730 rates, higher conversion rates, whatever your 00:22:16.730 --> 00:22:19.029 metric is. Document those improvement percentages. 00:22:19.390 --> 00:22:22.309 Prove its value. Okay, performance metrics. And 00:22:22.309 --> 00:22:25.309 third. Third is the chaos monkey test. Fun name, 00:22:25.410 --> 00:22:28.430 right? Yeah. What's that? This is edge case testing. 00:22:28.650 --> 00:22:31.509 You intentionally try to break your prompt, feed 00:22:31.509 --> 00:22:34.410 it weird input, nonsensical variables, unexpected 00:22:34.410 --> 00:22:37.470 data types, things users might accidentally do. 00:22:37.609 --> 00:22:39.750 Why? To make sure it handles errors gracefully. 00:22:40.140 --> 00:22:42.220 Does it give a helpful error message? Does it 00:22:42.220 --> 00:22:44.220 have a sensible fallback response? Or does it 00:22:44.220 --> 00:22:46.940 just crash or worse, give you completely bizarre, 00:22:47.180 --> 00:22:49.920 unusable output? You need to know how it fails. 00:22:50.160 --> 00:22:52.420 Okay. Consistency, performance, chaos monkey. 00:22:52.559 --> 00:22:54.319 That's the initial testing. What about the long 00:22:54.319 --> 00:22:56.539 game? Right. The long game is continuous improvement. 00:22:56.740 --> 00:22:58.779 Your AI system is never truly finished because 00:22:58.779 --> 00:23:00.619 the AI models themselves are always evolving. 00:23:00.859 --> 00:23:03.160 So like monthly reviews? Yeah, something like 00:23:03.160 --> 00:23:05.619 that. Monthly reviews. Look at your analytics. 00:23:06.539 --> 00:23:08.859 Which prompts are performing best? Which ones 00:23:08.859 --> 00:23:11.480 are generating the most value? Find the patterns 00:23:11.480 --> 00:23:13.480 in your winners, the structural elements, the 00:23:13.480 --> 00:23:16.200 phrasing, and use those insights to update your 00:23:16.200 --> 00:23:19.619 entire prompt library. Level everything up. In 00:23:19.619 --> 00:23:22.319 the longer term. Quarterly optimization. The 00:23:22.319 --> 00:23:25.500 AI landscape changes fast. New models come out. 00:23:25.539 --> 00:23:28.240 New features get added. So every quarter, you 00:23:28.240 --> 00:23:30.759 should probably test the latest AI models and 00:23:30.759 --> 00:23:33.160 capabilities. That state -of -the -art prompt 00:23:33.160 --> 00:23:35.420 you wrote three months ago, it might already 00:23:35.420 --> 00:23:37.619 be obsolete or inefficient compared to what's 00:23:37.619 --> 00:23:40.890 possible now. Wow. Okay, so you need to archive 00:23:40.890 --> 00:23:43.490 underperforming prompts. Yep. Archive the old 00:23:43.490 --> 00:23:45.769 ones. Rebuild prompts to take advantage of new 00:23:45.769 --> 00:23:48.589 AI features. Keep it current. It sounds like 00:23:48.589 --> 00:23:50.970 a lot of work, but necessary if you want a robust 00:23:50.970 --> 00:23:53.269 system. Now, you mentioned pitfalls earlier. 00:23:53.529 --> 00:23:55.529 Things that can kill your system. Ah, yes. The 00:23:55.529 --> 00:23:57.349 rookie mistakes. Think of them like things a 00:23:57.349 --> 00:23:59.509 beginner chef might mess up. Okay, what's pitfall 00:23:59.509 --> 00:24:02.269 number one? Pitfall one. The overly complicated 00:24:02.269 --> 00:24:05.980 recipe. This is trying to build incredibly elaborate, 00:24:06.180 --> 00:24:08.859 deeply nested JSON structures right from the 00:24:08.859 --> 00:24:11.259 start. You think more detail is always better, 00:24:11.299 --> 00:24:13.559 but... It can confuse the AI. Exactly. Sometimes 00:24:13.559 --> 00:24:16.460 simpler is better. The fix. Start simple. Get 00:24:16.460 --> 00:24:19.299 the basics working reliably. Then add complexity 00:24:19.299 --> 00:24:22.240 gradually, only when it's truly necessary to 00:24:22.240 --> 00:24:24.460 get the control you need. Okay, start simple. 00:24:24.559 --> 00:24:27.559 What's next? Pitfall 2. The hard -coded menu. 00:24:27.779 --> 00:24:30.059 This is where you hard -code specific details 00:24:30.059 --> 00:24:32.720 like a particular product name or a date directly 00:24:32.720 --> 00:24:35.599 into the JSON prompt template itself. Why is 00:24:35.599 --> 00:24:38.119 that bad? Because it turns what should be a scalable 00:24:38.119 --> 00:24:40.819 reusable template into a rigid one -off command. 00:24:40.980 --> 00:24:44.339 You can't easily adapt it. The fix. Always ask 00:24:44.339 --> 00:24:46.740 yourself which parts of this need to be changeable 00:24:46.740 --> 00:24:49.859 variables and which parts are fixed rules. Clearly 00:24:49.859 --> 00:24:52.200 separate the flexible inputs from the fixed structure. 00:24:52.539 --> 00:24:54.740 Makes sense. Keep templates flexible. Pitfall 00:24:54.740 --> 00:24:58.210 3. Pitfall 3. Serving an untasted dish. This 00:24:58.210 --> 00:25:00.170 is the big one, assuming your prompt works perfectly 00:25:00.170 --> 00:25:02.210 without actually testing it rigorously. Just 00:25:02.210 --> 00:25:05.789 writing it and deploying it. Yeah. Dangerous. 00:25:05.789 --> 00:25:08.849 The fix is obvious. That'll test every single 00:25:08.849 --> 00:25:10.990 prompt before you rely on it in a real system 00:25:10.990 --> 00:25:14.029 or for a client. Use that three -part framework. 00:25:14.589 --> 00:25:16.410 Consistency, performance, and the chaos monkey 00:25:16.410 --> 00:25:19.369 test. No excuses. Test everything. Got it. And 00:25:19.369 --> 00:25:22.730 the last one. Pitfall four. Using last year's 00:25:22.730 --> 00:25:24.910 recipe book. This is related to the continuous 00:25:24.910 --> 00:25:27.309 improvement point. It's using the same prompts 00:25:27.309 --> 00:25:30.150 indefinitely while the underlying AI models are 00:25:30.150 --> 00:25:32.490 evolving at a breathtaking pace. Sticking with 00:25:32.490 --> 00:25:34.950 what worked six months ago. Right. That prompt 00:25:34.950 --> 00:25:37.589 you perfected for GPT -4 might be inefficient 00:25:37.589 --> 00:25:40.349 or suboptimal with GPT -5 or whatever comes next. 00:25:40.529 --> 00:25:43.170 The fix. Regularly review and update your prompt 00:25:43.170 --> 00:25:45.490 library. Treat it like a living document, not 00:25:45.490 --> 00:25:48.220 a stone tablet carved once and forgotten. OK, 00:25:48.319 --> 00:25:50.559 those are four really practical pitfalls to watch 00:25:50.559 --> 00:25:52.579 out for. So wrapping this up, what's the ultimate 00:25:52.579 --> 00:25:54.740 takeaway here for listeners really looking to 00:25:54.740 --> 00:25:56.960 thrive in this whole AI era? I think it's this. 00:25:57.059 --> 00:25:59.880 The future belongs to the system builders, those 00:25:59.880 --> 00:26:02.680 who can translate ideas, needs and goals into 00:26:02.680 --> 00:26:05.119 structured data that AI can act upon reliably. 00:26:05.460 --> 00:26:08.599 System builders, not just prompt pinkers. That's 00:26:08.599 --> 00:26:11.059 a really powerful summary. It feels like the 00:26:11.059 --> 00:26:15.519 age of just casually typing random prompts, you 00:26:15.519 --> 00:26:18.250 know, hoping for a good result. Yeah. That's 00:26:18.250 --> 00:26:20.210 shifting. Maybe it's already over for serious 00:26:20.210 --> 00:26:23.150 applications. I think it is. Yeah. And JSON prompting 00:26:23.150 --> 00:26:25.950 isn't just some, I don't know, hack. It feels 00:26:25.950 --> 00:26:28.009 more fundamental. Like the bridge between those 00:26:28.009 --> 00:26:31.150 messy one -off outputs we all get sometimes and 00:26:31.150 --> 00:26:33.750 the structured repeatable systems that actually 00:26:33.750 --> 00:26:36.890 form the backbone of real defensible businesses 00:26:36.890 --> 00:26:40.589 using AI. It truly is that bridge. When you learn 00:26:40.589 --> 00:26:42.670 to think like a computer or at least communicate 00:26:42.670 --> 00:26:45.569 with it systematically using JSON, you stop wasting 00:26:45.569 --> 00:26:47.670 time and money on inconsistent results. Right. 00:26:47.789 --> 00:26:50.250 You start creating actual predictable value through 00:26:50.250 --> 00:26:52.869 repeatable workflows, reliable content pipelines, 00:26:53.150 --> 00:26:55.630 and those monetizable tools and services we talked 00:26:55.630 --> 00:26:58.369 about. And the best part maybe is that a powerful 00:26:58.369 --> 00:27:01.549 JSON template becomes this asset, right? It works 00:27:01.549 --> 00:27:04.220 for you 24 -7, churning out. Consistent quality. 00:27:04.420 --> 00:27:06.819 Exactly. It's your strategic advantage. It's 00:27:06.819 --> 00:27:09.579 your shortcut to quality and maybe your way out 00:27:09.579 --> 00:27:12.480 of that junk content land. So where is this heading? 00:27:12.559 --> 00:27:14.839 What's next for JSON prompting? Well, looking 00:27:14.839 --> 00:27:17.140 ahead, I think we'll see JSON prompts starting 00:27:17.140 --> 00:27:20.019 to coordinate multimodal stuff, text, image, 00:27:20.220 --> 00:27:23.319 video, audio, all potentially within a single 00:27:23.319 --> 00:27:26.809 structured command. Imagine asking for a blog 00:27:26.809 --> 00:27:29.170 post and the accompanying social media images 00:27:29.170 --> 00:27:32.210 in one go. Wow. OK. We'll probably also see more 00:27:32.210 --> 00:27:34.990 automated optimization. Right. Maybe AI systems 00:27:34.990 --> 00:27:37.769 that can fine tune their own JSON prompts based 00:27:37.769 --> 00:27:40.910 on real time performance data. Whoa. AI optimizing 00:27:40.910 --> 00:27:44.599 AI prompts. Yeah. And eventually. Probably more 00:27:44.599 --> 00:27:46.740 industry standardization around these structured 00:27:46.740 --> 00:27:49.220 formats, making it easier for different AI systems 00:27:49.220 --> 00:27:51.700 and tools to talk to each other seamlessly. Okay, 00:27:51.759 --> 00:27:53.819 so for listeners who want to get started, what's 00:27:53.819 --> 00:27:56.059 the essential toolkit? What do they need? Well, 00:27:56.119 --> 00:27:58.640 obviously the AI platforms themselves. ChatGPT 00:27:58.640 --> 00:28:01.359 is great for text. Claude is strong on complex 00:28:01.359 --> 00:28:04.299 reasoning. Gemini has powerful multimodal capabilities. 00:28:04.619 --> 00:28:07.240 Pick the right tool for the job. Right. And then 00:28:07.240 --> 00:28:09.279 some basic development tools can be really helpful. 00:28:09.460 --> 00:28:11.779 Things like online JSON validators, just to quickly 00:28:11.779 --> 00:28:14.119 check if your syntax, your brackets and commas 00:28:14.119 --> 00:28:17.000 are all correct. Saves a lot of headaches. Good 00:28:17.000 --> 00:28:20.599 tip. And maybe a simple code editor, even something 00:28:20.599 --> 00:28:25.279 basic like VS Code or Notepad++, just to create, 00:28:25.420 --> 00:28:28.779 organize, and manage your library of JSON prompts 00:28:28.779 --> 00:28:31.759 more easily than using a plain text file. Okay. 00:28:31.799 --> 00:28:34.619 And we should mention detailed examples of those 00:28:34.619 --> 00:28:37.240 JSON structures we discussed, like for the product 00:28:37.240 --> 00:28:39.599 description and logo generator, will be available 00:28:39.599 --> 00:28:41.460 in our show notes for people to look at. Definitely 00:28:41.460 --> 00:28:43.430 worth checking out. So maybe a final thought 00:28:43.430 --> 00:28:45.569 for our listeners. If you're serious about leveraging 00:28:45.569 --> 00:28:48.730 AI for profit or even just for reliable results 00:28:48.730 --> 00:28:51.269 in your work, it really feels like it's time 00:28:51.269 --> 00:28:53.690 to stop just playing with prompts and start engineering 00:28:53.690 --> 00:28:55.690 systems. Well said. Because that's where the 00:28:55.690 --> 00:28:58.130 future is being built, isn't it? So the question 00:28:58.130 --> 00:29:01.150 for you is, what system could you start designing 00:29:01.150 --> 00:29:05.230 today? Good question to ponder. Thanks for joining 00:29:05.230 --> 00:29:05.950 us for this deep dive.