WEBVTT 00:00:00.000 --> 00:00:02.140 Okay, let's untack this. We've all used ChatGPT, 00:00:02.240 --> 00:00:04.219 right? Typed in a quick question, maybe asked 00:00:04.219 --> 00:00:06.160 it to summarize something. But if that's what 00:00:06.160 --> 00:00:08.380 you're doing, you're really just scratching the 00:00:08.380 --> 00:00:11.339 surface, maybe tapping into, what, 10 % of its 00:00:11.339 --> 00:00:14.839 actual power, the rest, that 90 % most users 00:00:14.839 --> 00:00:17.420 just ignore. That's where it stops being a fancy 00:00:17.420 --> 00:00:19.660 search engine and starts becoming, well, your 00:00:19.660 --> 00:00:22.440 operational core. Exactly. And that's the mission 00:00:22.440 --> 00:00:24.660 for this deep dive. We've got some great sources 00:00:24.660 --> 00:00:27.420 that lay out a really comprehensive roadmap for 00:00:27.420 --> 00:00:29.699 achieving that mastery. We're talking about moving 00:00:29.699 --> 00:00:33.140 way past basic questions and answers towards 00:00:33.140 --> 00:00:36.700 treating AI like a proper operating system, you 00:00:36.700 --> 00:00:38.399 know, something that can build custom agents 00:00:38.399 --> 00:00:41.320 for you, manage huge amounts of data and automate 00:00:41.320 --> 00:00:45.170 some really serious high leverage work. This 00:00:45.170 --> 00:00:47.329 deep dive, we've structured it around a kind 00:00:47.329 --> 00:00:49.109 of upgrade path. We'll kick off with the new 00:00:49.109 --> 00:00:52.070 engine, the sophisticated thinking part of GPT 00:00:52.070 --> 00:00:55.929 -5. Then the crucial input secret, this structure 00:00:55.929 --> 00:00:58.829 called RTC -ROS. You really need to know this. 00:00:58.869 --> 00:01:01.130 Oh, absolutely. Next, we'll dive into autonomous 00:01:01.130 --> 00:01:04.489 action agents and deep research, taking action. 00:01:04.689 --> 00:01:07.209 And finally, we'll look at how you actually integrate 00:01:07.209 --> 00:01:10.010 all this into your digital life using things 00:01:10.010 --> 00:01:14.010 like connectors and custom GPTs. So, segment 00:01:14.010 --> 00:01:16.920 one. Let's talk about this new engine, GPT -5, 00:01:17.040 --> 00:01:19.959 and what's called cognitive amplification. You 00:01:19.959 --> 00:01:22.700 know, previous models, the large language models, 00:01:22.900 --> 00:01:25.319 they were stunningly good at predicting the next 00:01:25.319 --> 00:01:27.939 word. That was basically their main trick. GPT 00:01:27.939 --> 00:01:30.060 -5, though, seems to fundamentally shift things 00:01:30.060 --> 00:01:33.219 by adding this structured cognitive amplification 00:01:33.219 --> 00:01:35.680 layer. It's designed to actually think, to deliberate, 00:01:35.920 --> 00:01:38.280 before it spits out an answer, moving beyond 00:01:38.280 --> 00:01:40.719 just those shallow instant responses we're used 00:01:40.719 --> 00:01:42.780 to. Yeah, and what's really fascinating is how 00:01:42.780 --> 00:01:45.060 this deliberation shows up in practice. It gives 00:01:45.060 --> 00:01:47.659 you three sort of modes to interact with. You've 00:01:47.659 --> 00:01:49.439 still got instant mode for the quick stuff, you 00:01:49.439 --> 00:01:51.099 know, facts, simple questions. That's your traditional 00:01:51.099 --> 00:01:53.260 quick query. Then there's auto mode, which is 00:01:53.260 --> 00:01:55.359 pretty smart. It looks at how complex your request 00:01:55.359 --> 00:01:57.400 is and automatically decides how much thinking 00:01:57.400 --> 00:02:00.040 time it needs. It budgets its resources. Okay, 00:02:00.120 --> 00:02:02.099 and then there's the third one, thinking mode. 00:02:02.659 --> 00:02:05.420 This sounds like where the real power is, especially 00:02:05.420 --> 00:02:07.379 if you're tackling something complex. That's 00:02:07.379 --> 00:02:10.000 exactly right. Thinking mode engages deep reasoning. 00:02:10.520 --> 00:02:13.759 It's perfect for, like, complex problems, strategic 00:02:13.759 --> 00:02:16.460 planning, detailed market analysis. It's not 00:02:16.460 --> 00:02:18.039 just about getting an answer fast. It's about 00:02:18.039 --> 00:02:21.080 quality control. You can almost see the AI working 00:02:21.080 --> 00:02:23.819 through the problem step by step. It runs internal 00:02:23.819 --> 00:02:27.159 checks, considers different angles before giving 00:02:27.159 --> 00:02:29.699 you a comprehensive solution, this kind of self 00:02:29.699 --> 00:02:32.020 -correction. That's cognitive amplification in 00:02:32.020 --> 00:02:34.379 action. And it's not just text anymore, is it? 00:02:35.020 --> 00:02:38.000 fully into the multimodal revolution now. ChatGPT 00:02:38.000 --> 00:02:39.580 can handle different kinds of data, different 00:02:39.580 --> 00:02:41.780 file types, all at once. You just drag and drop 00:02:41.780 --> 00:02:44.439 files, basically. Yeah, it's pretty wild. You 00:02:44.439 --> 00:02:46.360 can upload an Excel spreadsheet, ask it to run 00:02:46.360 --> 00:02:49.199 a specific pivot table analysis, and then, say, 00:02:49.259 --> 00:02:51.500 summarize the findings in a 10 -bullet outline 00:02:51.500 --> 00:02:55.520 for a presentation. Or drop in a huge 50 -page 00:02:55.520 --> 00:02:57.699 PDF and ask it to pull out the key insights. 00:02:58.219 --> 00:03:00.740 Upload complex engineering images, get detailed 00:03:00.740 --> 00:03:03.719 descriptions. It instantly becomes your own personal 00:03:03.719 --> 00:03:08.560 data analyst and content creator. I have to admit, 00:03:08.639 --> 00:03:11.360 I still sometimes wrestle with the initial cognitive 00:03:11.360 --> 00:03:14.300 load, like deciding which mode to use or which 00:03:14.300 --> 00:03:16.379 file type is best, especially when I'm under 00:03:16.379 --> 00:03:18.360 pressure. Getting the hang of it is definitely 00:03:18.360 --> 00:03:20.560 a process. Sometimes figuring out if you need 00:03:20.560 --> 00:03:23.439 auto or full -on thinking mode takes a bit of 00:03:23.439 --> 00:03:25.300 trial and error. Mastery doesn't happen overnight. 00:03:25.620 --> 00:03:27.180 Right. So we've always kind of heard that these 00:03:27.180 --> 00:03:29.219 LLMs are just, you know, predictive text on steroids. 00:03:29.379 --> 00:03:32.500 They process data incredibly fast. If GPT -5 00:03:32.500 --> 00:03:34.819 can genuinely think, how is that really different 00:03:34.819 --> 00:03:37.139 from its predecessors just processing data fast? 00:03:37.229 --> 00:03:39.750 What's the fundamental shift? I think the key 00:03:39.750 --> 00:03:41.849 difference is that deliberation cycle. It's like 00:03:41.849 --> 00:03:44.530 it runs a meta -analysis on its own potential 00:03:44.530 --> 00:03:47.909 answer. It asks itself, is there a flaw in this 00:03:47.909 --> 00:03:50.870 approach? Should I maybe consider path B instead? 00:03:51.030 --> 00:03:54.050 It's evaluating its own process. Got it. So it's 00:03:54.050 --> 00:03:56.110 checking its own work before finalizing. That 00:03:56.110 --> 00:03:59.810 makes sense. Okay. Segment two. The input secret 00:03:59.810 --> 00:04:03.699 mastering this ARCHI CROS framework. This seems 00:04:03.699 --> 00:04:05.580 like the real high leverage knowledge here because 00:04:05.580 --> 00:04:07.520 you can have the best AI engine ever, right? 00:04:07.599 --> 00:04:10.120 But apparently 99 % of people fail because their 00:04:10.120 --> 00:04:13.199 prompts are just, well, terrible, vague and generic. 00:04:13.400 --> 00:04:16.220 And the quality of the output you get is always 00:04:16.220 --> 00:04:17.779 going to be directly tied to the quality of the 00:04:17.779 --> 00:04:20.060 input you give it. Garbage in, garbage out, basically. 00:04:20.259 --> 00:04:22.019 Couldn't have said it better. And that's precisely 00:04:22.019 --> 00:04:25.579 where the RTC -ROS framework comes in. Seriously. 00:04:25.579 --> 00:04:27.579 Learning this structure is probably the single 00:04:27.579 --> 00:04:30.220 most important skill. It stands for role, task, 00:04:30.459 --> 00:04:32.699 context, reasoning, output format, and stopping 00:04:32.699 --> 00:04:35.420 condition. What it does is eliminate all that 00:04:35.420 --> 00:04:38.360 ambiguity. It really directs the AI towards giving 00:04:38.360 --> 00:04:40.819 you something actionable, something high quality. 00:04:41.079 --> 00:04:42.839 Let's break that down then, because it sounds 00:04:42.839 --> 00:04:45.579 like specificity is absolutely key. The first 00:04:45.579 --> 00:04:49.879 R is for role. So you assign the AI a specific 00:04:49.879 --> 00:04:52.540 job, a persona. Don't just ask a general question. 00:04:52.660 --> 00:04:55.500 Tell it to act as a... say, professional travel 00:04:55.500 --> 00:04:58.199 planner or a senior business analyst. Is that 00:04:58.199 --> 00:05:01.399 right? Precisely. Then T is for task. That's 00:05:01.399 --> 00:05:04.160 the core mission, but defined with detail. Not 00:05:04.160 --> 00:05:07.220 just plan a trip, but create a detailed, viable 00:05:07.220 --> 00:05:10.120 three -day itinerary focusing specifically on 00:05:10.120 --> 00:05:12.399 historical landmarks. Really spell it out. Then 00:05:12.399 --> 00:05:15.439 C is for context. And this sounds vital. This 00:05:15.439 --> 00:05:16.720 is where you give it the specific constraints, 00:05:16.839 --> 00:05:18.639 the background information, like there are four 00:05:18.639 --> 00:05:20.180 of us, we're all vegetarian, here's our exact 00:05:20.180 --> 00:05:22.379 budget, and we're looking for a peaceful, maybe 00:05:22.379 --> 00:05:24.790 remote adventure. Definitely not the party scene. 00:05:25.069 --> 00:05:28.290 Exactly. The more context, the better. Then the 00:05:28.290 --> 00:05:31.230 next R is reasoning. This tells the AI how to 00:05:31.230 --> 00:05:34.089 think or what sources to use. Are you asking 00:05:34.089 --> 00:05:36.750 it to lean on the latest 2025 economic trends? 00:05:37.069 --> 00:05:39.670 Should it deliberately avoid common tourist traps? 00:05:39.990 --> 00:05:42.089 Maybe only use information published in the last 00:05:42.089 --> 00:05:44.819 six months? You guide its logic? Then O is the 00:05:44.819 --> 00:05:47.120 output format. Be specific about what you need 00:05:47.120 --> 00:05:49.399 back. A shareable PDF guide doesn't need specific 00:05:49.399 --> 00:05:51.600 sections like a table of costs. Spell that out. 00:05:51.879 --> 00:05:54.560 And finally, S is the stopping condition. This 00:05:54.560 --> 00:05:56.779 is about making the output manageable. Limit 00:05:56.779 --> 00:05:59.180 it. Ask for, say, only the top three or four 00:05:59.180 --> 00:06:01.040 recommendations maximum so you're not overwhelmed. 00:06:01.339 --> 00:06:03.519 Wow, okay. The difference in practice must be 00:06:03.519 --> 00:06:06.379 huge. You gave the example of a bad prompt. Create 00:06:06.379 --> 00:06:08.560 an itinerary for a three -day Goa trip. Yeah, 00:06:08.620 --> 00:06:12.019 and that gets you... Whoa! Usually generic, often 00:06:12.019 --> 00:06:14.519 pretty useless results, maybe some blog post 00:06:14.519 --> 00:06:18.500 summaries. But the detailed RTCRS prompt. The 00:06:18.500 --> 00:06:21.180 one specifying the role, expert planner. The 00:06:21.180 --> 00:06:24.579 context, vegetarian, peaceful, budget. The specific 00:06:24.579 --> 00:06:28.439 output, PDF guide. That's like, um... The difference 00:06:28.439 --> 00:06:30.620 between shouting a vague request into a crowded 00:06:30.620 --> 00:06:33.360 room versus having a focused conversation with 00:06:33.360 --> 00:06:35.500 a local expert who knows exactly what you like 00:06:35.500 --> 00:06:37.540 and need. Mind -blowing difference. Okay, that 00:06:37.540 --> 00:06:39.560 makes perfect sense. So if I'm just starting 00:06:39.560 --> 00:06:41.639 out and I only have time to really master one 00:06:41.639 --> 00:06:44.399 part of RTCOS this week, which one gives the 00:06:44.399 --> 00:06:46.319 biggest bang for the buck? The quickest win. 00:06:46.879 --> 00:06:49.800 Good question. I'd say assigning a specific expert 00:06:49.800 --> 00:06:52.779 role and providing really detailed context. Those 00:06:52.779 --> 00:06:54.939 two give the AI the most clarity up front and 00:06:54.939 --> 00:06:56.600 probably save you the most back and forth later. 00:06:56.740 --> 00:06:58.920 Clarity through role and context. Got it. Saves 00:06:58.920 --> 00:07:00.839 iterations. You got it. All right. Segment three, 00:07:01.000 --> 00:07:04.139 automation and action. Moving beyond just getting 00:07:04.139 --> 00:07:06.379 information to actually doing things. We're talking 00:07:06.379 --> 00:07:09.800 at agent mode. Explain this. Agent mode allows 00:07:09.800 --> 00:07:13.240 the AI to actually control a browser. and perform 00:07:13.240 --> 00:07:17.259 real -world multi -step tasks. This sounds like 00:07:17.259 --> 00:07:19.300 where the AI starts becoming an active assistant. 00:07:19.620 --> 00:07:22.740 It really is. And yeah, this feature, it's usually 00:07:22.740 --> 00:07:24.800 part of a premium subscription, so that's important 00:07:24.800 --> 00:07:27.660 context, but it's where the AI handles complex 00:07:27.660 --> 00:07:30.839 tasks for you. Think about planning a complicated 00:07:30.839 --> 00:07:33.300 trip. You could tell Agent Mode. Find the absolute 00:07:33.300 --> 00:07:35.740 cheapest return flights from Bangalore to London, 00:07:35.980 --> 00:07:38.100 traveling sometime between December and February, 00:07:38.319 --> 00:07:40.939 but the total trip duration must be under 10 00:07:40.939 --> 00:07:42.889 days. And instead of just giving the advisor 00:07:42.889 --> 00:07:45.889 links, the agent actually goes and does the searching. 00:07:45.970 --> 00:07:48.310 Exactly. We'll literally open up Google Flights, 00:07:48.430 --> 00:07:50.930 input your criteria, filter by the dates and 00:07:50.930 --> 00:07:53.569 duration you specified, compare prices across 00:07:53.569 --> 00:07:55.949 different airlines, and then it comes back with 00:07:55.949 --> 00:07:58.170 direct booking links for the best options it 00:07:58.170 --> 00:08:01.009 found. It automates what could be hours of tedious 00:08:01.009 --> 00:08:03.910 manual searching, doing it in just minutes. Okay. 00:08:03.949 --> 00:08:06.459 And you mentioned parallel processing. I could 00:08:06.459 --> 00:08:08.279 have multiple agents running at the same time, 00:08:08.300 --> 00:08:10.339 like one agent searching for those flights while 00:08:10.339 --> 00:08:12.939 another finds boutique hotels matching my specific 00:08:12.939 --> 00:08:16.759 aesthetic preferences, and maybe a third analyzes 00:08:16.759 --> 00:08:19.160 local transport options at the destination. That's 00:08:19.160 --> 00:08:20.980 the advanced strategy, yeah. You orchestrate 00:08:20.980 --> 00:08:22.980 multiple agents to tackle different parts of 00:08:22.980 --> 00:08:26.160 a complex project simultaneously. Huge time saver. 00:08:26.300 --> 00:08:29.160 Wow. Okay, then there's also deep research mode. 00:08:29.560 --> 00:08:31.620 How is that different from agent mode? Right, 00:08:31.779 --> 00:08:34.549 they're distinct. Agents act, they perform tasks 00:08:34.549 --> 00:08:36.850 in the digital world like booking or searching. 00:08:37.330 --> 00:08:39.549 Deep research mode, on the other hand, performs 00:08:39.549 --> 00:08:41.830 deep analysis. It generates these incredibly 00:08:41.830 --> 00:08:44.929 comprehensive reports. This is where that PhD 00:08:44.929 --> 00:08:48.230 level analysis in minutes promise really comes 00:08:48.230 --> 00:08:50.529 into play. That sounds almost too good to be 00:08:50.529 --> 00:08:52.850 true. Is there a catch? Like, does deep research 00:08:52.850 --> 00:08:56.289 mode ever, you know, hallucinate? Yeah. Or get 00:08:56.289 --> 00:08:58.269 biased in the sources it picks when it's running 00:08:58.269 --> 00:09:00.610 these huge reports? That's a really critical 00:09:00.610 --> 00:09:03.809 question. And the honest answer is yes, it absolutely 00:09:03.809 --> 00:09:06.490 can suffer from those issues. Hallucination and 00:09:06.490 --> 00:09:09.850 bias are still risks. But the key, the high leverage 00:09:09.850 --> 00:09:12.250 knowledge here is understanding that your prompt 00:09:12.250 --> 00:09:14.690 needs to include specific instructions for source 00:09:14.690 --> 00:09:16.909 verification. You have to tell it things like 00:09:16.909 --> 00:09:19.789 only use peer reviewed journals published after 00:09:19.789 --> 00:09:23.720 2020 or Cross -reference findings across at least 00:09:23.720 --> 00:09:26.799 three independent news sources. It's about leveraging 00:09:26.799 --> 00:09:29.419 its incredible speed while imposing your quality 00:09:29.419 --> 00:09:32.440 control standards. You guide the research process. 00:09:32.899 --> 00:09:35.779 Whoa. Okay, but imagine getting that right. Having 00:09:35.779 --> 00:09:38.100 like a virtual team of PhDs working for you. 00:09:38.480 --> 00:09:41.000 Generating a 20, maybe 30 -page market analysis 00:09:41.000 --> 00:09:43.679 report in, what, 10 or 15 minutes? A report that 00:09:43.679 --> 00:09:46.519 reviews over 100 sources, includes a full SWOT 00:09:46.519 --> 00:09:49.179 analysis, provides actionable strategies, maybe 00:09:49.179 --> 00:09:51.940 on a really complex topic like, say, youth spending 00:09:51.940 --> 00:09:53.460 habits in France. I mean, that's the kind of 00:09:53.460 --> 00:09:55.460 research consulting firms charge tens of thousands 00:09:55.460 --> 00:09:57.960 of dollars for. Exactly. It fundamentally shifts 00:09:57.960 --> 00:10:00.039 the cost. The bottleneck isn't time anymore. 00:10:00.240 --> 00:10:02.559 It's the quality of your initial request, your 00:10:02.559 --> 00:10:05.480 prompt engineering. So back to agent mode for 00:10:05.480 --> 00:10:08.120 a second. Can it be used for something really 00:10:08.120 --> 00:10:11.340 serious like financial decisions? Could I ask 00:10:11.340 --> 00:10:14.039 it to research a specific investment fund for 00:10:14.039 --> 00:10:17.190 me? Yes, definitely. It can analyze real -time 00:10:17.190 --> 00:10:20.269 fund performance data, scrape and summarize expert 00:10:20.269 --> 00:10:22.950 opinions from financial websites, and even provide 00:10:22.950 --> 00:10:25.029 personalized investment recommendations based 00:10:25.029 --> 00:10:27.269 on the risk profile you provided. Personalized 00:10:27.269 --> 00:10:29.330 recommendations. Okay. Based on my profile. Again, 00:10:29.429 --> 00:10:31.830 with the caveat that you need to verify and apply 00:10:31.830 --> 00:10:33.970 your own judgment, but the capability is there. 00:10:34.090 --> 00:10:36.250 Right. Okay, let's move to the final pillar, 00:10:36.350 --> 00:10:38.850 segment four, integration and customization. 00:10:39.580 --> 00:10:41.480 You mentioned connector power. This sounds like 00:10:41.480 --> 00:10:43.600 making ChatGPT the central hub for everything 00:10:43.600 --> 00:10:46.279 else I use, allowing the AI to talk to other 00:10:46.279 --> 00:10:49.639 applications like Canva, Gmail, Google Calendar, 00:10:49.799 --> 00:10:52.720 even GitHub, through plugins or maybe native 00:10:52.720 --> 00:10:55.299 integrations. That's the idea. It breaks the 00:10:55.299 --> 00:10:58.320 AI out of its silo. And this enables some really 00:10:58.320 --> 00:11:00.759 powerful automation workflows by chaining tools 00:11:00.759 --> 00:11:04.620 together. Imagine this. You ask ChatGPT to create 00:11:04.620 --> 00:11:07.480 a presentation on a complex topic. It first does 00:11:07.480 --> 00:11:09.379 the research, then it creates a detailed outline, 00:11:09.500 --> 00:11:11.600 and then it uses a connector to generate the 00:11:11.600 --> 00:11:14.179 actual presentation slides directly in Canva 00:11:14.179 --> 00:11:16.559 for you. All you need to do is maybe some minor 00:11:16.559 --> 00:11:19.179 polishing. Think about the hours of manual design 00:11:19.179 --> 00:11:21.799 work and copy pasting that saves. That's incredibly 00:11:21.799 --> 00:11:24.460 useful. Then there are custom GPTs. This is about 00:11:24.460 --> 00:11:26.519 building your own personalized AI mini apps, 00:11:26.600 --> 00:11:29.769 right? And crucially... without needing to write 00:11:29.769 --> 00:11:31.830 code. You gave the example of a LinkedIn content 00:11:31.830 --> 00:11:34.750 creator GPT. Trained on my specific brand voice, 00:11:34.889 --> 00:11:37.389 maybe even on posts from creators I admire. Exactly. 00:11:37.490 --> 00:11:40.330 Or maybe you build a business analyst GPT. You 00:11:40.330 --> 00:11:42.730 train it specifically on your company's internal 00:11:42.730 --> 00:11:46.509 KPIs, its goals, its past reports. So all the 00:11:46.509 --> 00:11:48.809 analysis and suggestions it provides are directly 00:11:48.809 --> 00:11:51.809 relevant to your operational reality. The secret 00:11:51.809 --> 00:11:53.750 sauce here isn't just that you can train it. 00:11:53.789 --> 00:11:57.429 It's how you feed it precise, high quality documents, 00:11:57.690 --> 00:11:59.870 your best work, your style guides, your data 00:11:59.870 --> 00:12:02.690 that dictate its tone, its knowledge base, its 00:12:02.690 --> 00:12:05.970 expertise. And for these custom assistants to 00:12:05.970 --> 00:12:09.100 really. work well long term, you need personalization 00:12:09.100 --> 00:12:11.419 and something called memory management. Crucial. 00:12:11.600 --> 00:12:13.980 Absolutely crucial. You need to customize the 00:12:13.980 --> 00:12:16.899 AI's personality. Do you want it witty? Strictly 00:12:16.899 --> 00:12:19.470 professional. Ultra concise. And you need to 00:12:19.470 --> 00:12:22.070 ensure it remembers key static details about 00:12:22.070 --> 00:12:23.610 you. Things like your professional background, 00:12:23.830 --> 00:12:25.629 your preferred citation style if you're academic, 00:12:25.850 --> 00:12:28.190 maybe even your location or dietary needs if 00:12:28.190 --> 00:12:29.870 it's helping with planning. When it has that 00:12:29.870 --> 00:12:32.049 consistent personalized memory, it stops feeling 00:12:32.049 --> 00:12:34.549 like a generic tool and starts acting like a 00:12:34.549 --> 00:12:36.370 truly bespoke assistant that understands you. 00:12:36.470 --> 00:12:38.710 Makes sense. Even image generation is getting 00:12:38.710 --> 00:12:41.110 more advanced, you're saying. It's not just basic 00:12:41.110 --> 00:12:43.149 prompts anymore. You can combine web research 00:12:43.149 --> 00:12:47.279 with visual creation, like asking it to... create 00:12:47.279 --> 00:12:49.759 a photorealistic advertisement for the new iPhone, 00:12:50.000 --> 00:12:52.659 place it on a bustling Bangalore airport road, 00:12:52.840 --> 00:12:56.039 and write some clever localized copywriting for 00:12:56.039 --> 00:12:59.159 it. So it's integrating research, context, and 00:12:59.159 --> 00:13:01.879 visual accuracy all at once. Yeah, the integration 00:13:01.879 --> 00:13:04.399 across modalities is getting really tight. Research 00:13:04.399 --> 00:13:07.480 informs creation, informs analysis. Okay, here's 00:13:07.480 --> 00:13:09.299 a practical question then. Let's say I've spent 00:13:09.299 --> 00:13:12.100 months carefully training my custom GPT, maybe 00:13:12.100 --> 00:13:16.059 that specialized business analyst GPT. How do 00:13:16.059 --> 00:13:18.360 I stop a one -off kind of sensitive request? 00:13:18.779 --> 00:13:20.860 Maybe ask it for ideas for a surprise party. 00:13:21.039 --> 00:13:23.419 How do I stop that random query from accidentally 00:13:23.419 --> 00:13:25.899 messing up the core personality or the business 00:13:25.899 --> 00:13:28.659 focus I've so carefully built? Ah, good question. 00:13:28.860 --> 00:13:31.179 You use the temporary chat mode feature for that. 00:13:31.340 --> 00:13:33.679 Think of it like an isolated sandbox. You can 00:13:33.679 --> 00:13:35.580 have that one -off conversation about the surprise 00:13:35.580 --> 00:13:37.340 party in temporary mode, and it won't affect 00:13:37.340 --> 00:13:39.419 the long -term memory, personality, or focus 00:13:39.419 --> 00:13:42.399 of your main personalized GPT profile. Keeps 00:13:42.399 --> 00:13:45.220 things clean. Temporary chat mode. Got it. Like 00:13:45.220 --> 00:13:47.820 an incognito window for AI. Pretty much, yeah. 00:13:48.399 --> 00:13:51.200 Okay, let's wrap up. Right. So to summarize the 00:13:51.200 --> 00:13:52.919 big transformation here, you're really moving 00:13:52.919 --> 00:13:55.879 from using ChatGPT like a better search engine 00:13:55.879 --> 00:13:59.419 or a simple tool to treating it as a comprehensive 00:13:59.419 --> 00:14:02.659 AI operating system, something that can research 00:14:02.659 --> 00:14:04.419 like a team of analysts, create like a design 00:14:04.419 --> 00:14:07.059 agency, analyze complex data, and learn like 00:14:07.059 --> 00:14:09.659 a personal tutor just for you. And the keys to 00:14:09.659 --> 00:14:11.620 making that happen, the success principles, are 00:14:11.620 --> 00:14:15.360 specificity using frameworks like RTC -ROS, iteration 00:14:15.360 --> 00:14:17.159 refining your prompts, integration connecting 00:14:17.159 --> 00:14:18.889 it to your other tools. tools, and automation 00:14:18.889 --> 00:14:21.669 using agents and workflows. And the actionable 00:14:21.669 --> 00:14:24.429 roadmap for listeners, for you, is pretty clear 00:14:24.429 --> 00:14:27.570 then. Start by really mastering that RTC ROS 00:14:27.570 --> 00:14:30.309 prompting framework this week. That's going to 00:14:30.309 --> 00:14:32.509 give you immediate noticeable improvement in 00:14:32.509 --> 00:14:35.309 your results. Then maybe next week, focus on 00:14:35.309 --> 00:14:37.629 setting up your first agent for a recurring task, 00:14:37.830 --> 00:14:40.769 or start building and optimizing your first custom 00:14:40.769 --> 00:14:44.080 GPT. The tools are largely there. Many are free 00:14:44.080 --> 00:14:46.179 or have free tiers. They're incredibly powerful. 00:14:46.600 --> 00:14:48.659 And while lots of people are still just typing 00:14:48.659 --> 00:14:51.279 basic questions, you now have the knowledge to 00:14:51.279 --> 00:14:53.419 build automated workflows that execute complex, 00:14:53.620 --> 00:14:56.639 high -value tasks. That's the edge. And think 00:14:56.639 --> 00:14:58.419 about this, a final provocative thought, maybe. 00:14:58.779 --> 00:15:01.259 If the AI can now be trained to know your specific 00:15:01.259 --> 00:15:03.500 research methodology, your preferred citation 00:15:03.500 --> 00:15:06.120 style, and can handle the entire research and 00:15:06.120 --> 00:15:08.639 citation process for you, does the competitive 00:15:08.639 --> 00:15:11.159 advantage in knowledge work shift entirely? Away 00:15:11.159 --> 00:15:13.460 from who can find the data fastest, towards who 00:15:13.460 --> 00:15:15.879 can engineer the absolute best, most insightful, 00:15:16.120 --> 00:15:18.659 most strategic question in the first place? That 00:15:18.659 --> 00:15:20.700 is definitely something to chew on. The power 00:15:20.700 --> 00:15:23.139 shifting from finding answers to formulating 00:15:23.139 --> 00:15:25.679 questions. For now, get specific this week. Try 00:15:25.679 --> 00:15:28.840 RTC -ROS. and watch the quality of your AI interaction 00:15:28.840 --> 00:15:29.600 skyrocket.