WEBVTT 00:00:00.000 --> 00:00:02.980 What if I told you there's a simple trick, a 00:00:02.980 --> 00:00:06.320 way to have AI write its own perfect one -shot 00:00:06.320 --> 00:00:09.300 prompts for you? Imagine never struggling with 00:00:09.300 --> 00:00:12.480 AI prompts again. Welcome to the Deep Dive. We're 00:00:12.480 --> 00:00:14.820 your shortcut to, well, really understanding 00:00:14.820 --> 00:00:16.960 this stuff. And today, yeah, we're jumping into 00:00:16.960 --> 00:00:19.160 something that honestly feels like a cheat code 00:00:19.160 --> 00:00:23.079 for AI. It's called reverse metaprompting. Reverse 00:00:23.079 --> 00:00:24.960 metaprompting. Okay. Think about it, right? How 00:00:24.960 --> 00:00:28.390 many times have you spent, like... hours tweaking 00:00:28.390 --> 00:00:30.670 an AI prompt, you finally get that perfect result. 00:00:30.829 --> 00:00:32.609 And then what? We'll just move on. You move on. 00:00:32.630 --> 00:00:34.929 You found this amazing treasure map, but you 00:00:34.929 --> 00:00:37.509 kind of just toss it aside. So this deep dive 00:00:37.509 --> 00:00:40.350 is all about how to capture that map every single 00:00:40.350 --> 00:00:42.750 time. Okay. I like that. And our journey today 00:00:42.750 --> 00:00:44.289 is going to cover exactly that, right? We'll 00:00:44.289 --> 00:00:47.310 look at how this technique works across the board, 00:00:47.409 --> 00:00:50.810 you know, text generation, images, even video. 00:00:52.390 --> 00:00:54.689 sophisticated voice agents too. Absolutely. And 00:00:54.689 --> 00:00:56.990 even building whole applications. The end goal 00:00:56.990 --> 00:01:00.509 is creating your own personal, really valuable 00:01:00.509 --> 00:01:02.609 database of what you might call super prompts. 00:01:02.750 --> 00:01:05.530 Right. Okay. So let's unpack this a bit. We all 00:01:05.530 --> 00:01:07.920 know that feeling, the struggle. Yeah. You're 00:01:07.920 --> 00:01:09.719 trying to get the AI to do something specific, 00:01:09.840 --> 00:01:12.319 and it just turns into this endless back and 00:01:12.319 --> 00:01:15.400 forth. It really does. You prompt, you get something 00:01:15.400 --> 00:01:17.939 okay, you tweak it, it gets a bit better, tweak 00:01:17.939 --> 00:01:20.879 again, finally you nail it. Yeah. But reverse 00:01:20.879 --> 00:01:24.219 metaprompting flips that. Instead of guessing 00:01:24.219 --> 00:01:27.120 at the good prompt from the start, we work backward. 00:01:27.200 --> 00:01:30.060 We start from that proven successful result. 00:01:30.439 --> 00:01:33.260 Ah, okay. So it's like having a time machine. 00:01:33.560 --> 00:01:35.420 Exactly like a time machine. You finish this 00:01:35.420 --> 00:01:38.120 long journey, lots of detours, you know. But 00:01:38.120 --> 00:01:40.680 instead of just celebrating you got there, you 00:01:40.680 --> 00:01:43.560 use this technique to go back to the start and 00:01:43.560 --> 00:01:46.920 ask the AI, hey, what was the perfect map? The 00:01:46.920 --> 00:01:48.560 one that would have gotten me here straight away. 00:01:48.719 --> 00:01:51.359 In one smooth step. Yeah. And the command for 00:01:51.359 --> 00:01:55.579 this? Is it complex? Surprisingly simple. After 00:01:55.579 --> 00:01:57.599 you get that perfect output, you just tell the 00:01:57.599 --> 00:02:00.159 AI something like, analyze our entire conversation. 00:02:00.420 --> 00:02:02.540 Now act like a world -class prompt engineer. 00:02:03.310 --> 00:02:06.510 And write a single well -structured prompt that 00:02:06.510 --> 00:02:08.909 would produce your last output immediately if 00:02:08.909 --> 00:02:12.770 I used it first. Boom. You extract the DNA of 00:02:12.770 --> 00:02:14.930 that perfect result. So it's reverse engineering 00:02:14.930 --> 00:02:17.650 its own best input based on our success. Exactly. 00:02:17.909 --> 00:02:21.509 The AI literally tells you the perfect instructions 00:02:21.509 --> 00:02:24.150 it needed. That's a fascinating twist. So we're 00:02:24.150 --> 00:02:26.310 essentially teaching the AI by showing it what 00:02:26.310 --> 00:02:29.169 works. Is this about getting the AI to learn 00:02:29.169 --> 00:02:33.520 from us? Yes, precisely. AI extracts its own 00:02:33.520 --> 00:02:35.879 perfect prompt from our successful iterations. 00:02:36.280 --> 00:02:38.060 Okay, let's get practical. How does this work 00:02:38.060 --> 00:02:41.120 with, say, just text? This is where you can teach 00:02:41.120 --> 00:02:43.280 the AI your personal style, right? Yeah, this 00:02:43.280 --> 00:02:45.000 is where it gets really cool for personalization. 00:02:45.500 --> 00:02:48.199 Imagine you start with a pretty basic prompt, 00:02:48.400 --> 00:02:51.580 maybe, write a brief about an AI fitness app 00:02:51.580 --> 00:02:54.280 for beginners. Okay, standard stuff. Right. The 00:02:54.280 --> 00:02:57.080 first output might be fine, functional, but... 00:02:57.680 --> 00:02:59.500 Kind of generic. Maybe the sentences are all 00:02:59.500 --> 00:03:01.219 the same length, a bit monotonous. Yeah, I've 00:03:01.219 --> 00:03:03.560 seen that. So you'd refine it. Exactly. You'd 00:03:03.560 --> 00:03:05.419 give specific feedback. Maybe something like, 00:03:05.479 --> 00:03:08.219 make this more detailed. Vary the sentence structure 00:03:08.219 --> 00:03:11.159 more. Mix in long sentences, short ones, maybe 00:03:11.159 --> 00:03:13.719 some bullet points. You're giving it a lesson. 00:03:13.919 --> 00:03:17.819 Teaching it your rhythm, your style. And once 00:03:17.819 --> 00:03:20.199 it gets it, once the output feels right, feels 00:03:20.199 --> 00:03:22.500 like you. And you hit it with that reverse meta 00:03:22.500 --> 00:03:24.659 prompt command. You got it. Analyze our chat. 00:03:24.819 --> 00:03:27.039 Give me the one shot prompt. And it spits out 00:03:27.039 --> 00:03:29.199 a new prompt. But this time it includes those 00:03:29.199 --> 00:03:33.840 style rules explicitly. Like very sentence structure 00:03:33.840 --> 00:03:36.719 dramatically. Exactly. Mix short, punchy sentences 00:03:36.719 --> 00:03:39.060 with longer, complex ones. Things like that. 00:03:39.180 --> 00:03:41.539 It becomes a reusable template that already has 00:03:41.539 --> 00:03:44.180 your voice baked in. This really lets us infuse 00:03:44.180 --> 00:03:47.930 our personal style into AI outputs. Consistently. 00:03:47.930 --> 00:03:50.569 Yes. It codifies your unique preferences into 00:03:50.569 --> 00:03:53.129 a reusable template. Okay. That makes sense for 00:03:53.129 --> 00:03:56.680 text. But visual AI images, that could be a whole 00:03:56.680 --> 00:03:58.439 different kind of headache. Like, how can this 00:03:58.439 --> 00:04:01.580 help prevent those really frustrating errors 00:04:01.580 --> 00:04:03.520 like text getting cut off in an infographic? 00:04:04.039 --> 00:04:06.259 Yeah, the classic cut off text problem. Well, 00:04:06.300 --> 00:04:08.680 a good technique first is priming. Priming. Yeah. 00:04:08.780 --> 00:04:10.919 Before you ask for the image, you ask the AI 00:04:10.919 --> 00:04:13.800 to, like, do some research. For an infographic, 00:04:14.120 --> 00:04:16.420 you might say, first, go research how to design 00:04:16.420 --> 00:04:18.779 a beautiful infographic like a world class designer 00:04:18.779 --> 00:04:20.600 would. You sort of set the stage for quality. 00:04:20.839 --> 00:04:23.910 OK, smart. But even then. It can still mess up, 00:04:24.009 --> 00:04:26.050 right? Yeah. You ask for a clear and elegant 00:04:26.050 --> 00:04:28.209 infographic and maybe it looks nice, but half 00:04:28.209 --> 00:04:30.269 the text is missing at the bottom. Happens all 00:04:30.269 --> 00:04:32.370 the time. Super frustrating, wastes credits, 00:04:32.569 --> 00:04:35.410 wastes time. So after you've iterated and fixed 00:04:35.410 --> 00:04:37.889 it, maybe told it, make sure all text is visible, 00:04:38.050 --> 00:04:40.310 then you use the reverse meta prompt. But what 00:04:40.310 --> 00:04:42.870 do you ask it for an image fix? Something like, 00:04:42.949 --> 00:04:45.410 how could I have avoided the back and forth on 00:04:45.410 --> 00:04:46.889 this image? How could I have gotten it right 00:04:46.889 --> 00:04:50.490 the first time? Generate a concise to the point 00:04:50.490 --> 00:04:52.939 image prompt that would have produced. that final 00:04:52.939 --> 00:04:55.540 correct image immediately. And what does the 00:04:55.540 --> 00:04:58.019 new prompt look like? Is it just like, don't 00:04:58.019 --> 00:05:00.040 cut off text? It's usually much more technical 00:05:00.040 --> 00:05:01.899 than you'd think. It might include specifics 00:05:01.899 --> 00:05:04.620 we wouldn't normally think to add. Things like 00:05:04.620 --> 00:05:08.079 vertical layout, maybe exact color codes, or 00:05:08.079 --> 00:05:11.899 crucially, complete size details or aspect ratios. 00:05:12.360 --> 00:05:14.500 Ah, so it gives us technical commands we wouldn't 00:05:14.500 --> 00:05:16.779 necessarily know are important. Exactly. It adds 00:05:16.779 --> 00:05:19.220 technical specifics for precise, error -free 00:05:19.220 --> 00:05:23.110 image generation. Okay, let's talk video. Capturing 00:05:23.110 --> 00:05:26.209 creative nuances there, that feels even harder 00:05:26.209 --> 00:05:28.769 sometimes. It's like directing, needing multiple 00:05:28.769 --> 00:05:31.769 takes. That's a great analogy. It really is like 00:05:31.769 --> 00:05:34.110 being a film director giving notes to an actor. 00:05:34.250 --> 00:05:37.129 You might start with a prompt that's, you know, 00:05:37.189 --> 00:05:40.889 creative but a bit vague. Something like, middle 00:05:40.889 --> 00:05:43.649 -aged man relaxing in a rooftop lounge at sunset, 00:05:43.910 --> 00:05:46.689 enjoying a glowing lavender lemonade. Sounds 00:05:46.689 --> 00:05:49.069 nice, but maybe the first video isn't quite right. 00:05:49.110 --> 00:05:51.550 Exactly. The first take might be good, but it 00:05:51.550 --> 00:05:53.730 misses details. So you give your director's note. 00:05:53.889 --> 00:05:56.110 Like specific tweaks. Yeah. Okay, create one 00:05:56.110 --> 00:05:58.009 with an actual lemon slice sticking out of the 00:05:58.009 --> 00:06:00.730 drink. Then maybe now add some sparkling reflections 00:06:00.730 --> 00:06:03.569 inside the glass. You keep refining, take after 00:06:03.569 --> 00:06:05.629 take, until it matches your vision. And then, 00:06:05.750 --> 00:06:09.250 once it's perfect, the magic question. You got 00:06:09.250 --> 00:06:12.209 it. I love the last video. What kind of prompt 00:06:12.209 --> 00:06:14.350 could I have written to get this exact same result 00:06:14.350 --> 00:06:16.990 from the start? And the AI includes all those 00:06:16.990 --> 00:06:20.310 old notes in the new prompt. Yes. The new prompt 00:06:20.310 --> 00:06:22.629 will incorporate all those refinements. It might 00:06:22.629 --> 00:06:25.910 specify glowing lavender lemonade garnished with 00:06:25.910 --> 00:06:29.029 a fresh lemon slice or details about the reflections. 00:06:29.069 --> 00:06:31.649 It captures that nuance. Which saves a lot of 00:06:31.649 --> 00:06:34.370 time and potentially expensive generation credits 00:06:34.370 --> 00:06:37.149 down the line. Absolutely. Does this save actual 00:06:37.149 --> 00:06:41.110 money on AI generation? Yes. By achieving the 00:06:41.110 --> 00:06:44.269 desired video in a single optimized attempt. 00:06:44.970 --> 00:06:46.730 Okay, there's something else interesting here, 00:06:46.810 --> 00:06:49.610 a sort of hidden benefit you've mentioned, learning 00:06:49.610 --> 00:06:52.069 the AI's language. Yeah, I like to think of it 00:06:52.069 --> 00:06:54.470 as a kind of language immersion program for prompt 00:06:54.470 --> 00:06:57.610 engineering. As you go through this process with 00:06:57.610 --> 00:07:01.180 different tools, text. Images, video, the AI, 00:07:01.420 --> 00:07:04.319 through these reverse meta prompts, inadvertently 00:07:04.319 --> 00:07:06.759 taches you. Taches you what? The specific vocabulary, 00:07:07.060 --> 00:07:09.100 the kind of words that work best for that type 00:07:09.100 --> 00:07:10.839 of creation. You start picking up words maybe 00:07:10.839 --> 00:07:12.500 you wouldn't normally use. Can you give some 00:07:12.500 --> 00:07:15.199 examples? Sure. Maybe descriptive words like 00:07:15.199 --> 00:07:19.339 jauntily or effervescent or vibrant hues or even 00:07:19.339 --> 00:07:22.000 technical terms like specific camera angles or 00:07:22.000 --> 00:07:25.680 lighting types for images in video. It subtly 00:07:25.680 --> 00:07:28.420 shifts you from just a good prompter to a potentially 00:07:28.420 --> 00:07:30.740 great one. Because you're learning the nuances 00:07:30.740 --> 00:07:33.319 of the AI's preferred language for each task. 00:07:33.660 --> 00:07:37.139 Exactly. What's fascinating here is this isn't 00:07:37.139 --> 00:07:39.500 just about being more efficient. It's about genuine 00:07:39.500 --> 00:07:42.199 learning, kind of absorbing expertise. Whoa. 00:07:42.439 --> 00:07:45.540 Imagine truly speaking the AI's language for 00:07:45.540 --> 00:07:48.259 any creative field, like being fluent in AI image 00:07:48.259 --> 00:07:50.600 speak or AI video speak. That's kind of mind 00:07:50.600 --> 00:07:52.600 -blowing. Does this make us better prompt engineers 00:07:52.600 --> 00:07:55.819 universally? Yes, it naturally expands your professional 00:07:55.819 --> 00:07:58.639 vocabulary for all AI platforms. Okay, let's 00:07:58.639 --> 00:08:00.420 shift gears again. Voice agents, these seem like 00:08:00.420 --> 00:08:02.220 a really unique opportunity for this kind of 00:08:02.220 --> 00:08:03.879 feedback. You compared it to coaching. Yeah, 00:08:03.920 --> 00:08:05.800 like a coach reviewing game tape. Voice agents 00:08:05.800 --> 00:08:07.439 are great because they often generate detailed 00:08:07.439 --> 00:08:09.839 logs of their interactions. Right, the call logs. 00:08:10.459 --> 00:08:13.500 Often JSON files, you said. Structured data. 00:08:13.839 --> 00:08:16.680 Exactly. Structured records, complete transcripts 00:08:16.680 --> 00:08:19.000 of the user and the agent talking. That's your 00:08:19.000 --> 00:08:22.220 game tape. So the film room is feeding these 00:08:22.220 --> 00:08:25.600 logs back into another, maybe more powerful AI, 00:08:25.800 --> 00:08:29.480 like Clod 4 or GPT -5. Precisely. You feed it 00:08:29.480 --> 00:08:31.699 the log where something went wrong, and you use 00:08:31.699 --> 00:08:34.220 a reverse metaprompt to figure out why and how 00:08:34.220 --> 00:08:36.559 to fix it in the agent's core programming, its 00:08:36.559 --> 00:08:40.460 system prompt. So if the agent gave wrong information... 00:08:40.460 --> 00:08:43.179 You might ask the auditing AI. Okay, the agent 00:08:43.179 --> 00:08:45.740 made this factual mistake here. How can we tweak 00:08:45.740 --> 00:08:48.059 the system prompt so this specific error doesn't 00:08:48.059 --> 00:08:50.220 happen again? Or if the conversation flow felt 00:08:50.220 --> 00:08:53.139 awkward. You could say, hmm, the agent keeps 00:08:53.139 --> 00:08:55.100 asking if the caller is a beginner, even when 00:08:55.100 --> 00:08:56.960 it's not relevant. Look at this conversation. 00:08:57.539 --> 00:08:59.779 Suggest an amended version of the system prompt 00:08:59.779 --> 00:09:02.480 to make the flow smoother. And the output is 00:09:02.480 --> 00:09:05.250 essentially a new playbook. an updated system 00:09:05.250 --> 00:09:09.129 prompt exactly it creates this amazing self -improving 00:09:09.129 --> 00:09:12.049 feedback loop every conversation especially the 00:09:12.049 --> 00:09:14.590 flawed ones becomes direct input for making the 00:09:14.590 --> 00:09:17.210 agent better you know i have to admit i still 00:09:17.210 --> 00:09:19.669 wrestle with prompt drift myself when building 00:09:19.669 --> 00:09:22.210 voice agents getting them to stay consistent 00:09:22.210 --> 00:09:25.840 is tough so this feedback loop Yeah, it sounds 00:09:25.840 --> 00:09:28.039 absolutely golden for making them truly robust. 00:09:28.379 --> 00:09:32.120 So the AI effectively fixes its own past mistakes 00:09:32.120 --> 00:09:34.960 based on real interactions. Yes, it uses conversation 00:09:34.960 --> 00:09:37.580 logs to self -correct and improve. Okay, taking 00:09:37.580 --> 00:09:40.799 that idea, can we push it even further, make 00:09:40.799 --> 00:09:43.600 it fully automated, a self -healing system? We 00:09:43.600 --> 00:09:45.860 absolutely can. This is getting more advanced, 00:09:45.960 --> 00:09:48.080 but it's definitely achievable. You can set up 00:09:48.080 --> 00:09:50.940 a system that automatically scores conversations. 00:09:51.340 --> 00:09:53.950 Based on what? Metrics. Yeah, performance metrics 00:09:53.950 --> 00:09:56.009 you define. Maybe call duration, user sentiment, 00:09:56.250 --> 00:09:58.250 task completion rate, whatever matters. And if 00:09:58.250 --> 00:10:00.509 a conversation scores badly. Below a certain 00:10:00.509 --> 00:10:03.210 threshold, yeah. If it fails the quality check, 00:10:03.330 --> 00:10:05.789 that automatically triggers an optimization process. 00:10:06.210 --> 00:10:09.129 Don't tell me. It runs the reverse metaprompt. 00:10:09.289 --> 00:10:11.590 Automatically. It takes the log from the failed 00:10:11.590 --> 00:10:13.850 conversation, runs the reverse metaprompt to 00:10:13.850 --> 00:10:15.889 figure out a better system prompt, and then... 00:10:15.889 --> 00:10:17.850 It updates the agent without a human touching 00:10:17.850 --> 00:10:20.370 it. Seamlessly integrates the improved prompt 00:10:20.370 --> 00:10:23.129 back into the agent. You end up with this continuously 00:10:23.129 --> 00:10:27.269 improving, self -healing AI agent. Gets smarter 00:10:27.269 --> 00:10:30.169 over time on its own. Wow. And you mentioned 00:10:30.169 --> 00:10:32.690 this applies to ARAG chatbots too. Remind us 00:10:32.690 --> 00:10:35.850 what ARAG is again. Right. ARAG is retrieval 00:10:35.850 --> 00:10:38.690 augmented generation. Basically, AI that searches 00:10:38.690 --> 00:10:41.330 a specific knowledge base, like company documents, 00:10:41.570 --> 00:10:43.710 to find information and answer questions based 00:10:43.710 --> 00:10:45.850 on it. Okay. So how does self -healing work there? 00:10:46.090 --> 00:10:49.519 You can have an auditor AI. compare the chatbot's 00:10:49.519 --> 00:10:52.320 answers in the transcript against the original 00:10:52.320 --> 00:10:54.399 source documents it was supposed to use. Ah, 00:10:54.620 --> 00:10:56.759 checking its work. Exactly. It finds where the 00:10:56.759 --> 00:10:59.419 chatbot maybe pulled the wrong info or misinterpreted 00:10:59.419 --> 00:11:02.259 it. Then the reverse metaprompt helps refine 00:11:02.259 --> 00:11:04.580 the system prompt to improve how the chatbot 00:11:04.580 --> 00:11:07.139 finds and uses information from its knowledge 00:11:07.139 --> 00:11:10.000 base, making it more accurate. So this makes 00:11:10.000 --> 00:11:13.299 AI agents essentially autonomous in their improvement, 00:11:13.460 --> 00:11:15.659 learning, and repairing themselves. Yes, they 00:11:15.659 --> 00:11:17.580 learn and repair themselves automatically over 00:11:17.580 --> 00:11:20.799 time. Okay, this is powerful stuff. What about 00:11:20.799 --> 00:11:25.039 vibe coding? You know, building actual applications 00:11:25.039 --> 00:11:28.360 by talking to an AI. Can we use this reverse 00:11:28.360 --> 00:11:31.600 meta prompting to get like a reusable blueprint 00:11:31.600 --> 00:11:34.279 for a whole app, not just code snippets? Yes, 00:11:34.340 --> 00:11:37.100 absolutely. You can extract the entire learning 00:11:37.100 --> 00:11:39.299 journey, even for building something complex, 00:11:39.580 --> 00:11:43.620 into a single reusable blueprint. The key is 00:11:43.620 --> 00:11:45.720 the prompt you use after you've built the app. 00:11:46.139 --> 00:11:47.879 Through all that back and forth. What kind of 00:11:47.879 --> 00:11:49.799 prompt would that be? Something comprehensive. 00:11:50.220 --> 00:11:52.860 Like, okay, taking into account all our previous 00:11:52.860 --> 00:11:54.899 conversations, the mistakes we fixed, the bugs 00:11:54.899 --> 00:11:57.879 we squashed, write a single well -structured 00:11:57.879 --> 00:12:00.480 prompt that would save us all that time and gotten 00:12:00.480 --> 00:12:02.399 us here much faster. And you mentioned a key 00:12:02.399 --> 00:12:04.919 principle here, architect versus bricklayer. 00:12:05.000 --> 00:12:07.100 Right. You need to tell the AI to act like the 00:12:07.100 --> 00:12:09.740 architect, not the bricklayer. Meaning? High 00:12:09.740 --> 00:12:12.460 level plan, not low level details. Exactly. The 00:12:12.460 --> 00:12:14.580 architect gives you the blueprints. Yeah. The 00:12:14.580 --> 00:12:16.519 overall design, the structure, the materials, 00:12:16.600 --> 00:12:18.960 the approach. The brick layer just lays the bricks 00:12:18.960 --> 00:12:21.480 according to instructions. For complex app, you 00:12:21.480 --> 00:12:24.059 want the blueprint first. So you explicitly tell 00:12:24.059 --> 00:12:27.059 the AI not to give you code in this meta prompt. 00:12:27.399 --> 00:12:30.120 Yes. You'd add something like, do not give me 00:12:30.120 --> 00:12:32.600 the specific code in the new prompt. Tell me 00:12:32.600 --> 00:12:35.580 conceptually what the agent should do, what the 00:12:35.580 --> 00:12:38.259 main features are, and maybe suggest a technology 00:12:38.259 --> 00:12:41.639 stack. But stay high level. Don't get lost in 00:12:41.639 --> 00:12:43.799 the weeds of the code itself. And the output 00:12:43.799 --> 00:12:47.379 is the blueprint. Blueprint output, yeah. It 00:12:47.379 --> 00:12:49.519 describes the app's main functions, suggests 00:12:49.519 --> 00:12:51.860 technologies, maybe outlines the architecture. 00:12:52.039 --> 00:12:54.580 It gives you that strategic guidance without 00:12:54.580 --> 00:12:57.360 locking you into specific inflexible code that 00:12:57.360 --> 00:12:59.259 might need changing anyway. So it's really about 00:12:59.259 --> 00:13:01.139 capturing the high level plan, the architectural 00:13:01.139 --> 00:13:04.500 strategy, not the exact lines of code. Precisely. 00:13:04.639 --> 00:13:06.980 Focus on the architectural blueprint for flexibility 00:13:06.980 --> 00:13:09.600 and power. Okay, this feels like we're moving 00:13:09.600 --> 00:13:12.019 towards the end game now. Scaling this professionally, 00:13:12.179 --> 00:13:14.139 building real assets. Yeah, think about advanced 00:13:14.139 --> 00:13:16.659 applications. Maybe you've used AI to build a 00:13:16.659 --> 00:13:19.080 custom data analytics tool, something to analyze 00:13:19.080 --> 00:13:22.360 CSVs, perhaps replacing a tool like Tableau or 00:13:22.360 --> 00:13:24.580 Power BI for certain tasks. It's a complex project. 00:13:25.019 --> 00:13:27.000 Right. After you've built it through iteration, 00:13:27.340 --> 00:13:29.820 you use a powerful reverse metaprompt, maybe 00:13:29.820 --> 00:13:32.720 asking it to, go through this entire code base 00:13:32.720 --> 00:13:36.169 and create a comprehensive prompt. focusing on 00:13:36.169 --> 00:13:38.690 the infrastructure and the approach, not specific 00:13:38.690 --> 00:13:42.070 code, like a neural pathway for how an AI could 00:13:42.070 --> 00:13:44.429 build this whole thing in one shot. And the result 00:13:44.429 --> 00:13:46.789 is more than just a prompt. Much more. It's essentially 00:13:46.789 --> 00:13:50.269 a full project blueprint. Tech stack recommendations, 00:13:50.690 --> 00:13:54.509 key features, database schema ideas, maybe even 00:13:54.509 --> 00:13:57.470 API architecture suggestions. It's a strategic 00:13:57.470 --> 00:14:00.029 document you can reuse or adapt. And you can 00:14:00.029 --> 00:14:02.750 even break it down further. into sub -agents. 00:14:02.789 --> 00:14:05.830 Absolutely. You can then prompt the AI, OK, based 00:14:05.830 --> 00:14:07.710 on that blueprint, come up with examples of sub 00:14:07.710 --> 00:14:09.450 -agents that could work in parallel to build 00:14:09.450 --> 00:14:11.750 this. Defining specialized roles. Exactly. You 00:14:11.750 --> 00:14:13.929 get prompts for maybe a back -end agent, a front 00:14:13.929 --> 00:14:16.269 -end agent, a data processing agent, a testing 00:14:16.269 --> 00:14:19.549 agent. It maps out a whole virtual multi -agent 00:14:19.549 --> 00:14:22.789 development team. Wow. OK. And the final asset 00:14:22.789 --> 00:14:25.090 here, the ultimate goal. Is building your personal 00:14:25.090 --> 00:14:27.850 prompt repository, your own library of these 00:14:27.850 --> 00:14:30.230 optimized super prompts. Could be simple, like 00:14:30.230 --> 00:14:33.740 text files. Or more complex. Could be simple 00:14:33.740 --> 00:14:36.019 text files, could be a searchable database, whatever 00:14:36.019 --> 00:14:38.919 works for you. The point is, you're capturing 00:14:38.919 --> 00:14:41.399 everything. Capturing what specifically? Your 00:14:41.399 --> 00:14:43.919 technical specifications, your preferred style, 00:14:44.259 --> 00:14:46.659 all that hard -won knowledge you gained fixing 00:14:46.659 --> 00:14:50.000 mistakes and iterating. It creates this amazing 00:14:50.000 --> 00:14:53.500 compound learning effect. Each saved prompt makes 00:14:53.500 --> 00:14:55.980 you a better, more intuitive prompt engineer. 00:14:56.460 --> 00:14:58.899 So this library becomes our most valuable AI 00:14:58.899 --> 00:15:00.980 asset, right? Capturing our unique way of working 00:15:00.980 --> 00:15:03.460 with AI. Absolutely. It's your personal treasure 00:15:03.460 --> 00:15:07.220 chest of proven, optimized super prompts. Sponsor? 00:15:07.549 --> 00:15:09.509 Okay, let's just take a breath and unpack what 00:15:09.509 --> 00:15:11.590 we've really learned here. Reverse metaproncting. 00:15:11.909 --> 00:15:14.970 It's more than just a neat trick, isn't it? It 00:15:14.970 --> 00:15:17.129 feels like a fundamental shift. It really does. 00:15:17.269 --> 00:15:19.850 It transforms what can be frustrating, iterative 00:15:19.850 --> 00:15:23.009 work with AI into, well, into valuable, reusable 00:15:23.009 --> 00:15:25.549 knowledge. You're turning those discarded attempts 00:15:25.549 --> 00:15:28.509 into a compounding asset, building your own AI 00:15:28.509 --> 00:15:31.289 wisdom library. Yeah. We've seen how it works 00:15:31.289 --> 00:15:34.129 for injecting personal style in text, fixing 00:15:34.129 --> 00:15:36.289 those annoying image bugs, getting video nuances 00:15:36.289 --> 00:15:38.730 right, creating voice agents that actually improve 00:15:38.730 --> 00:15:40.889 themselves. And even generating architectural 00:15:40.889 --> 00:15:44.350 blueprints for whole applications. It saves time, 00:15:44.490 --> 00:15:47.009 definitely saves money on credit sometimes, and 00:15:47.009 --> 00:15:49.570 it just makes you better at this. If we connect 00:15:49.570 --> 00:15:52.179 this to the bigger picture. It feels like this 00:15:52.179 --> 00:15:55.080 method lets us truly partner with AI. We're not 00:15:55.080 --> 00:15:56.779 just barking commands. We're letting it teach 00:15:56.779 --> 00:15:59.700 us its optimal language. It's more of a dialogue. 00:15:59.960 --> 00:16:01.899 The dialogue where both sides are learning and 00:16:01.899 --> 00:16:05.139 improving. Yeah. So for everyone listening, your 00:16:05.139 --> 00:16:08.840 journey with this starts now. Next time you get 00:16:08.840 --> 00:16:11.740 that perfect AI result, don't just celebrate 00:16:11.740 --> 00:16:14.519 and close the window. No, extract the learning. 00:16:14.700 --> 00:16:17.519 Make it a deliberate step. Ask that simple but 00:16:17.519 --> 00:16:20.899 really game -changing question. What prompt would 00:16:20.899 --> 00:16:23.600 have delivered this perfect result in one single 00:16:23.600 --> 00:16:26.159 attempt? And save that answer. Save that optimized 00:16:26.159 --> 00:16:28.399 prompt in your repository, whatever form that 00:16:28.399 --> 00:16:30.879 takes. Use it as your starting point next time 00:16:30.879 --> 00:16:32.700 you do something similar. It sounds simple, but 00:16:32.700 --> 00:16:36.279 doing this consistently, it really will revolutionize 00:16:36.279 --> 00:16:38.539 your workflow, make you a master prompter almost 00:16:38.539 --> 00:16:40.500 by default. Which really raises an important 00:16:40.500 --> 00:16:42.740 question for you to think about. What hidden 00:16:42.740 --> 00:16:44.759 treasure maps are you currently throwing away 00:16:44.759 --> 00:16:47.720 in your daily AI conversations? You actually 00:16:47.720 --> 00:16:50.340 have the tools now to keep them. We really encourage 00:16:50.340 --> 00:16:52.539 you to start trying this, implementing this technique 00:16:52.539 --> 00:16:56.299 right away. The time you invest up front in capturing 00:16:56.299 --> 00:16:59.240 these prompts, it'll pay back tenfold easily. 00:16:59.440 --> 00:17:02.039 Gives you a real edge. Absolutely. Until next 00:17:02.039 --> 00:17:04.519 time, keep learning, keep exploring. Out to your 00:17:04.519 --> 00:17:04.920 own music.