WEBVTT 00:00:00.000 --> 00:00:03.560 Have you ever felt that profound frustration, 00:00:04.040 --> 00:00:07.580 trying to coax genuinely brilliant results from 00:00:07.580 --> 00:00:10.679 AI only to get back something? Well, kind of 00:00:10.679 --> 00:00:13.080 generic. Oh, damn good. I still wrestle with 00:00:13.080 --> 00:00:15.759 prompt drift myself, you know? Yeah. That feeling 00:00:15.759 --> 00:00:18.379 of sending the same instruction and getting slightly 00:00:18.379 --> 00:00:21.100 different, less useful outputs over time. Yeah, 00:00:21.239 --> 00:00:23.640 damn right. It turns out there's maybe a deeper 00:00:23.640 --> 00:00:27.320 secret to unlocking AI's true power than just 00:00:27.320 --> 00:00:30.289 crafting the perfect prompt. Welcome to the Deep 00:00:30.289 --> 00:00:34.649 Dive. We unpack complex ideas into clear, digestible 00:00:34.649 --> 00:00:37.130 insights, really revealing the crucial knowledge 00:00:37.130 --> 00:00:39.789 you need to stay ahead. Glad to be here. Today, 00:00:39.810 --> 00:00:41.890 we're diving into a concept that's rapidly changing 00:00:41.890 --> 00:00:44.969 how we work with artificial intelligence, context 00:00:44.969 --> 00:00:47.369 engineering. Yeah, this is a big one. It's essentially 00:00:47.369 --> 00:00:50.649 about teaching the AI about your world, right? 00:00:50.649 --> 00:00:53.170 Your specific universe. Not just firing off commands. 00:00:53.289 --> 00:00:55.189 Exactly. Not just isolated commands into the 00:00:55.189 --> 00:00:57.609 void. We're going to explore what context engineering 00:00:57.609 --> 00:01:00.250 is, how it fundamentally differs from traditional 00:01:00.250 --> 00:01:02.109 prompt engineering. Yeah, and show how it changes 00:01:02.109 --> 00:01:04.849 things. And show you exactly how it transforms 00:01:04.849 --> 00:01:09.079 AI outputs from just functional to, well, truly 00:01:09.079 --> 00:01:11.060 extraordinary. We'll walk through some compelling 00:01:11.060 --> 00:01:13.980 real -world examples. We'll tackle that intriguing 00:01:13.980 --> 00:01:16.840 Goldilocks problem. Yes, not too much, not too 00:01:16.840 --> 00:01:19.329 little. the challenge of providing just the right 00:01:19.329 --> 00:01:21.629 amount of information, and then we'll introduce 00:01:21.629 --> 00:01:23.810 a powerful framework to help you get that balance 00:01:23.810 --> 00:01:27.109 perfect. By the end of this deep dive, you should 00:01:27.109 --> 00:01:30.409 have a clear, actionable path to building AI 00:01:30.409 --> 00:01:32.829 workflows that truly understand what you need 00:01:32.829 --> 00:01:36.049 and deliver consistent, really impactful results. 00:01:36.609 --> 00:01:39.670 So many of us use powerful AI tools like ChatGPT 00:01:39.670 --> 00:01:42.870 or Cloud, daily maybe? Yeah, they're everywhere. 00:01:43.030 --> 00:01:47.510 But often the responses feel flat. A bit uninspired. 00:01:47.670 --> 00:01:49.950 Solace sometimes. Yeah. You might give it a simple 00:01:49.950 --> 00:01:52.090 command like, write a follow -up email, and what 00:01:52.090 --> 00:01:54.230 you get back is totally templated, generic enough 00:01:54.230 --> 00:01:56.109 to apply to anyone. Which means it connects with 00:01:56.109 --> 00:01:58.569 no one, really. Exactly. That's a super common 00:01:58.569 --> 00:02:00.849 experience, isn't it? The core issue is that 00:02:00.849 --> 00:02:03.689 AI, for all its brilliance, often operates in 00:02:03.689 --> 00:02:06.049 a vacuum. Kind of like a blank slate. No background 00:02:06.049 --> 00:02:08.930 knowledge. Right. It lacks the implicit knowledge, 00:02:09.069 --> 00:02:11.550 the tribal wisdom of your specific business, 00:02:11.729 --> 00:02:14.449 your unique customers, your goals. So even with 00:02:14.449 --> 00:02:18.569 a great prompt, it's basically guessing. Context 00:02:18.569 --> 00:02:21.169 engineering is about filling that void, giving 00:02:21.169 --> 00:02:24.770 the AI the nuanced understanding it needs to 00:02:24.770 --> 00:02:28.129 stop guessing and start truly. contributing. 00:02:28.229 --> 00:02:30.830 Like onboarding a new team member? Exactly. It's 00:02:30.830 --> 00:02:32.870 like providing a new team member with your entire 00:02:32.870 --> 00:02:35.050 company's institutional knowledge on day one. 00:02:35.129 --> 00:02:38.069 That's a powerful analogy onboarding an AI. So 00:02:38.069 --> 00:02:40.830 how does this environment building, this deep 00:02:40.830 --> 00:02:43.050 immersion, actually differ from what most people 00:02:43.050 --> 00:02:45.430 think of as basic prompt engineering? Well the 00:02:45.430 --> 00:02:47.509 fundamental shift is about connecting the AI 00:02:47.509 --> 00:02:51.639 to dynamic continuously updated information and 00:02:51.639 --> 00:02:54.180 crucially integrating it into multi -step business 00:02:54.180 --> 00:02:57.400 processes. So it's about making AI a truly informed 00:02:57.400 --> 00:03:00.280 dynamic partner in the process. Precisely. Moving 00:03:00.280 --> 00:03:02.539 from isolated commands to an AI that actually 00:03:02.539 --> 00:03:04.939 understands and acts within your workflow. OK. 00:03:04.979 --> 00:03:07.240 Let's unpack those two core differences then. 00:03:07.819 --> 00:03:10.280 First, you mentioned dynamic context. Traditional 00:03:10.280 --> 00:03:12.900 prompt engineering might put static info right 00:03:12.900 --> 00:03:15.000 in the prompt. Like write this in a professional 00:03:15.000 --> 00:03:17.400 tone. Right. Exactly. And that static approach, 00:03:17.419 --> 00:03:20.669 it has its place. Sure. But with context engineering, 00:03:20.930 --> 00:03:25.050 you connect the AI to live data sources. Live 00:03:25.050 --> 00:03:27.530 data? What do you mean? Imagine an always -updating 00:03:27.530 --> 00:03:31.330 Google Sheet. Maybe with daily customer interactions. 00:03:32.150 --> 00:03:35.430 Or your CRM system feeding real -time sales data. 00:03:35.490 --> 00:03:39.139 Wow, okay. Or even direct. real -time info streaming 00:03:39.139 --> 00:03:42.400 from APIs. This continuous data flow keeps the 00:03:42.400 --> 00:03:45.099 AI operating with the absolute freshest, most 00:03:45.099 --> 00:03:47.060 relevant information. And the second difference 00:03:47.060 --> 00:03:49.099 you mentioned, workflow integration. This is 00:03:49.099 --> 00:03:50.560 where I think it gets really interesting for 00:03:50.560 --> 00:03:52.680 businesses. Oh, this is truly where the magic 00:03:52.680 --> 00:03:55.219 happens. Context engineering doesn't just operate 00:03:55.219 --> 00:03:58.719 in isolation, you see. It's integrated into larger, 00:03:59.199 --> 00:04:01.860 often pretty complex business processes. Like 00:04:01.860 --> 00:04:04.219 a sales process. Exactly. Think about a 10 -step 00:04:04.219 --> 00:04:06.479 sales process. Maybe three or four of those steps 00:04:06.479 --> 00:04:09.800 involve AI agents. Each agent in that sequence 00:04:09.800 --> 00:04:14.020 accesses specific, relevant context for its particular 00:04:14.020 --> 00:04:17.160 step. And crucially, it builds on the outputs 00:04:17.160 --> 00:04:20.579 from the previous AI or even human steps. Ah, 00:04:20.579 --> 00:04:23.379 so they pass information along. Precisely. It 00:04:23.379 --> 00:04:26.220 creates this seamless, intelligent value chain 00:04:26.220 --> 00:04:28.860 that moves towards a defined business outcome. 00:04:29.139 --> 00:04:32.680 That idea of AI agents building on previous outputs. 00:04:33.250 --> 00:04:35.550 That's fascinating. Can you give us a concrete 00:04:35.550 --> 00:04:37.230 example of something that really brings it to 00:04:37.230 --> 00:04:40.029 life? Absolutely. Let's look at content marketing. 00:04:40.129 --> 00:04:43.709 We can compare the output of just a generic request 00:04:43.709 --> 00:04:47.110 versus a blog post generated with real context 00:04:47.110 --> 00:04:49.850 engineering. Okay, so dynamic context means fresh 00:04:49.850 --> 00:04:52.269 info, and workful integration means connected 00:04:52.269 --> 00:04:54.589 actions. You got it. Real -time intelligence 00:04:54.589 --> 00:04:57.910 powering interconnected strategic AI tasks. All 00:04:57.910 --> 00:04:59.889 right, so imagine you just tell your AI, write 00:04:59.889 --> 00:05:01.850 a blog post about the benefits of cloud computing 00:05:01.850 --> 00:05:03.930 for small businesses. Standard request. Right. 00:05:04.069 --> 00:05:06.129 The result, it'll probably be correct, technically 00:05:06.129 --> 00:05:08.829 sound. But boring. But almost certainly incredibly 00:05:08.829 --> 00:05:11.029 generic. Yeah. It's the kind of piece you read, 00:05:11.170 --> 00:05:13.069 you nod, and you immediately forget. It just 00:05:13.069 --> 00:05:15.410 doesn't resonate. That's the typical old way. 00:05:15.660 --> 00:05:18.920 a basic standalone request. Now, with context 00:05:18.920 --> 00:05:21.040 engineering, you don't just ask for a blog post. 00:05:21.459 --> 00:05:24.379 You build an entire workflow around it. A workflow? 00:05:24.720 --> 00:05:28.459 How? Well, your AI now has access to real -time 00:05:28.459 --> 00:05:31.620 data from your Google Search Console, so it knows 00:05:31.620 --> 00:05:34.220 the exact keywords your audience is actually 00:05:34.220 --> 00:05:36.579 searching for. Okay, that's smart. It pulls from 00:05:36.579 --> 00:05:38.720 your customer persona document. So it understands 00:05:38.720 --> 00:05:41.100 their pain points, their aspirations, their language. 00:05:41.860 --> 00:05:44.040 It consults your brand voice guide to make sure 00:05:44.040 --> 00:05:46.319 every word aligns with your company's identity. 00:05:46.339 --> 00:05:49.139 Right. And it can even access your latest product 00:05:49.139 --> 00:05:52.540 catalog, complete with features and pricing for 00:05:52.540 --> 00:05:55.300 specific offerings. Wow. So your prompt isn't 00:05:55.300 --> 00:05:57.300 just a command anymore. It becomes something 00:05:57.300 --> 00:05:59.819 far more powerful, almost like a detailed project 00:05:59.819 --> 00:06:02.519 brief for a human expert. Exactly. You're asking 00:06:02.519 --> 00:06:05.910 it to write a compelling 1 ,200 word blog post, 00:06:06.029 --> 00:06:08.730 maybe, but then you layer in all that critical 00:06:08.730 --> 00:06:12.490 context, specific GSE keyword data, precise customer 00:06:12.490 --> 00:06:15.629 pain points, your brand voice guidelines. And 00:06:15.629 --> 00:06:17.689 details on your unique product packages, like 00:06:17.689 --> 00:06:20.509 your starter cloud or growth engine. Specifics 00:06:20.509 --> 00:06:24.009 matter. The result isn't just an article. It's 00:06:24.009 --> 00:06:27.889 a profound, highly targeted piece. It speaks 00:06:27.889 --> 00:06:30.920 directly to your ideal customer. in their own 00:06:30.920 --> 00:06:33.259 language, addressing their specific challenges. 00:06:33.379 --> 00:06:35.899 And promoting your solutions. And subtly promoting 00:06:35.899 --> 00:06:38.660 your specific solutions. That's the transformative 00:06:38.660 --> 00:06:41.560 power. It moves from just general information 00:06:41.560 --> 00:06:45.360 to a real strategic asset. That content marketing 00:06:45.360 --> 00:06:47.360 example really clarifies things. It makes me 00:06:47.360 --> 00:06:50.839 wonder, how accessible is this really for an 00:06:50.839 --> 00:06:54.089 average business? How do we actually... build 00:06:54.089 --> 00:06:56.509 one of these, you know, sophisticated contextual 00:06:56.509 --> 00:06:58.970 workflows ourselves. It's actually more accessible 00:06:58.970 --> 00:07:01.089 than you might think. It basically involves four 00:07:01.089 --> 00:07:03.470 key steps. Four steps, okay. Setting up a trigger, 00:07:03.709 --> 00:07:06.310 adding an AI step, connecting those dynamic sources, 00:07:06.569 --> 00:07:08.949 and then crafting a truly contextual prompt. 00:07:09.110 --> 00:07:11.910 So context really takes a simple request and 00:07:11.910 --> 00:07:14.350 makes it strategic. Exactly. Turning generic 00:07:14.350 --> 00:07:16.550 ideas into profoundly relevant and effective 00:07:16.550 --> 00:07:18.370 communication. Okay, this is where the rubber 00:07:18.370 --> 00:07:20.610 meets the road then. What's step one for someone 00:07:20.610 --> 00:07:23.129 looking to implement this? Step one. Set up your 00:07:23.129 --> 00:07:25.089 trigger. This is the signal, right? The thing 00:07:25.089 --> 00:07:27.370 that kicks off your workflow. Like what? Could 00:07:27.370 --> 00:07:30.149 be a new lead appearing in your CRM, could be 00:07:30.149 --> 00:07:32.689 a contact form submission on your website, or 00:07:32.689 --> 00:07:34.670 even just a schedule time, like every Monday 00:07:34.670 --> 00:07:37.170 morning at 9 a .m. Okay, so the trigger defines 00:07:37.170 --> 00:07:39.910 when it starts. Exactly. When the AI springs 00:07:39.910 --> 00:07:43.069 into action. Then, step two, add your AI step. 00:07:43.470 --> 00:07:45.569 This is where you choose your AI model, maybe 00:07:45.569 --> 00:07:48.819 Claude III Sonnet for creative writing. or GPT 00:07:48.819 --> 00:07:51.220 -4 for analysis. Depending on the task. Right. 00:07:51.480 --> 00:07:53.980 But the magic that elevates this beyond just 00:07:53.980 --> 00:07:57.160 basic prompting, that really happens next, doesn't 00:07:57.160 --> 00:08:00.350 it? Right. Step three, connect dynamic context 00:08:00.350 --> 00:08:02.709 sources. This is where you plug in all that live 00:08:02.709 --> 00:08:06.170 relevant information. This can mean data from 00:08:06.170 --> 00:08:08.589 previous steps in this workflow. It could mean 00:08:08.589 --> 00:08:10.769 pulling in linked info from external tools like 00:08:10.769 --> 00:08:13.850 your CRM or customer support databases, or even 00:08:13.850 --> 00:08:16.490 dynamic knowledge files like Google Docs or Sheets 00:08:16.490 --> 00:08:18.970 that your team keeps updated. So living documents. 00:08:19.310 --> 00:08:21.370 Exactly. And you can even use more advanced stuff 00:08:21.370 --> 00:08:23.970 like model context protocol connections that 00:08:23.970 --> 00:08:26.550 allows for structured programmatic access to 00:08:26.639 --> 00:08:29.459 potentially vast internal data sources, so the 00:08:29.459 --> 00:08:32.919 AI can query and retrieve really specific information 00:08:32.919 --> 00:08:37.700 as needed. And finally, step four, craft your 00:08:37.700 --> 00:08:40.620 contextual prompt. OK, so now your prompt isn't 00:08:40.620 --> 00:08:44.179 just a request. No, it's much more. It's a meticulously 00:08:44.179 --> 00:08:46.919 designed set of instructions that integrates 00:08:46.919 --> 00:08:50.460 and leverages all that rich dynamic context you've 00:08:50.460 --> 00:08:52.340 provided. It's a blueprint. Yeah, like giving 00:08:52.340 --> 00:08:54.879 the AI a blueprint filled with precise, relevant 00:08:54.879 --> 00:08:57.240 details. Think about crafting a personalized 00:08:57.240 --> 00:08:59.519 follow -up email to a potential client. Okay. 00:08:59.840 --> 00:09:01.519 Instead of some generic template, you'd pull 00:09:01.519 --> 00:09:03.580 in the customer's exact name, their interaction 00:09:03.580 --> 00:09:06.580 history, right from your CRM. Specific pain points 00:09:06.580 --> 00:09:08.120 they mentioned, maybe from a knowledge file. 00:09:08.240 --> 00:09:11.039 and even the latest product updates from a Google 00:09:11.039 --> 00:09:14.500 Doc. The AI isn't guessing anymore. It's synthesizing 00:09:14.500 --> 00:09:17.559 current relevant data to create a truly bespoke 00:09:17.559 --> 00:09:20.019 message. That makes perfect sense. But I can 00:09:20.019 --> 00:09:22.639 imagine a potential pitfall here. Is there a 00:09:22.639 --> 00:09:26.120 danger of giving the AI too much information? 00:09:26.570 --> 00:09:29.210 Or maybe too little. Absolutely. That's precisely 00:09:29.210 --> 00:09:31.570 what we call the Goldilocks problem in context 00:09:31.570 --> 00:09:33.350 engineering. The Goldilocks problem. OK. You 00:09:33.350 --> 00:09:35.889 need it to be just right. So it's about carefully 00:09:35.889 --> 00:09:39.110 curating the right live information for each 00:09:39.110 --> 00:09:42.610 specific AI step, avoiding overload, but also 00:09:42.610 --> 00:09:45.950 scarcity. Yes, exactly. Making sure the AI has 00:09:45.950 --> 00:09:48.009 exactly what it needs when it needs it. Nothing 00:09:48.009 --> 00:09:50.649 more, nothing less. The Goldilocks problem. Finding 00:09:50.649 --> 00:09:53.309 that sweet spot. What exactly happens if you 00:09:53.309 --> 00:09:56.379 go too far? If you give an AI too much context. 00:09:57.340 --> 00:09:59.220 Several issues pop up, and they can be quite 00:09:59.220 --> 00:10:02.179 costly, actually. How so? First, higher financial 00:10:02.179 --> 00:10:05.600 costs. Most AI APIs charge by tokens. Tokens, 00:10:05.700 --> 00:10:07.840 right? Like little pieces of text? Exactly. Small 00:10:07.840 --> 00:10:10.639 pieces of text, words, or parts of words. Every 00:10:10.639 --> 00:10:13.240 bit of context you feed it adds to this token 00:10:13.240 --> 00:10:16.039 count. So verbose input directly hits your budget. 00:10:16.100 --> 00:10:18.340 OK, makes sense. This also seriously impacts 00:10:18.340 --> 00:10:20.840 the AI's context window. Which is like its short 00:10:20.840 --> 00:10:23.129 -term memory. Yeah, basically, the finite amount 00:10:23.129 --> 00:10:26.970 of text an AI model can hold in its active, immediate 00:10:26.970 --> 00:10:31.049 memory at one time overwhelm that window, and 00:10:31.049 --> 00:10:34.090 the AI just struggles to filter the noise. It 00:10:34.090 --> 00:10:36.629 gets less focused. So it loses track. It can 00:10:36.629 --> 00:10:38.330 lose the thread of your main instruction, yeah. 00:10:38.850 --> 00:10:40.830 And frustratingly, it can even increase the risk 00:10:40.830 --> 00:10:43.389 of hallucinations. Where it makes stuff up. Where 00:10:43.389 --> 00:10:48.179 the AI just invents information, exactly. Imagine 00:10:48.179 --> 00:10:51.379 scaling that to a billion queries, each one overloaded 00:10:51.379 --> 00:10:54.960 with data. The costs alone. That'd be staggering. 00:10:55.360 --> 00:10:58.179 Huge. So, giving it your entire 50 -page business 00:10:58.179 --> 00:11:00.039 plan just to write a short social media post 00:11:00.039 --> 00:11:03.539 is, well, counterproductive. Completely. What 00:11:03.539 --> 00:11:05.419 about the other side, then? What happens if you 00:11:05.419 --> 00:11:07.600 provide too little context? Well, that leads 00:11:07.600 --> 00:11:10.460 directly back to those generic kind of soulless 00:11:10.460 --> 00:11:12.100 responses we talked about earlier. Yeah, the 00:11:12.100 --> 00:11:14.659 flat stuff. There's a complete lack of personalization. 00:11:14.779 --> 00:11:18.240 and a much higher chance of the AI making incorrect 00:11:18.240 --> 00:11:20.259 assumptions because it simply doesn't have enough 00:11:20.259 --> 00:11:22.919 specifics to work with. Like asking for directions 00:11:22.919 --> 00:11:25.259 without saying where you want to go. Pretty much. 00:11:25.559 --> 00:11:27.820 It's like asking for a social media post without 00:11:27.820 --> 00:11:30.399 mentioning the product, or the audience, or the 00:11:30.399 --> 00:11:34.100 call to action. It's essentially useless. The 00:11:34.100 --> 00:11:35.980 Goldilocks problem isn't just about technical 00:11:35.980 --> 00:11:38.679 limits. It's really a strategic design challenge. 00:11:38.879 --> 00:11:41.679 It sounds like precise curation of context is 00:11:41.679 --> 00:11:44.500 just as crucial as the prompt itself, like turning 00:11:44.500 --> 00:11:47.240 AI from a blunt tool into a surgical instrument. 00:11:47.519 --> 00:11:50.679 Well put. So how do we find that sweet spot? 00:11:50.980 --> 00:11:53.460 The source mentions something called the real 00:11:53.460 --> 00:11:56.860 framework, R -E -A. Ah, yes. The real framework 00:11:56.860 --> 00:11:59.580 is your guide, basically, to providing perfect 00:11:59.580 --> 00:12:02.379 context. OK. What does it stand for? R is for 00:12:02.379 --> 00:12:05.269 relevant. only include information that directly 00:12:05.269 --> 00:12:08.350 helps the AI complete its specific task. No fluff. 00:12:08.549 --> 00:12:11.789 Got it. Relevance. E is for efficient. Provided 00:12:11.789 --> 00:12:14.250 concisely. No redundancy. Think maybe a one -page 00:12:14.250 --> 00:12:16.210 summary instead of that 20 -toach doc. Efficient. 00:12:16.330 --> 00:12:18.529 Okay. A is for accessible. Your context has to 00:12:18.529 --> 00:12:21.149 be readily available to the AI. Ideally, connecting 00:12:21.149 --> 00:12:23.610 to live databases or structured knowledge files, 00:12:23.789 --> 00:12:26.629 it can't be locked away. Accessible. Right. And 00:12:26.629 --> 00:12:29.629 L is for logical. Structured information works 00:12:29.629 --> 00:12:32.590 best. using clear formatting, like Markdown, 00:12:32.870 --> 00:12:34.970 perhaps, and consistent terminology throughout. 00:12:35.490 --> 00:12:38.710 Don't confuse it. Relevant, efficient, accessible, 00:12:39.230 --> 00:12:42.750 logical, real. So the real framework helps make 00:12:42.750 --> 00:12:45.149 sure our context is perfectly tuned. Exactly. 00:12:45.210 --> 00:12:48.450 It's about quality and precision over just sheer 00:12:48.450 --> 00:12:51.210 volume, making sure you get maximum impact. This 00:12:51.210 --> 00:12:53.009 brings us to another really crucial point, I 00:12:53.009 --> 00:12:55.389 think. Most people, when they first start, probably 00:12:55.389 --> 00:12:58.210 get this wrong. How should we actually organize 00:12:58.210 --> 00:13:00.690 and structure our knowledge files for the AI? 00:13:00.769 --> 00:13:03.309 So it's both logical and accessible. Yeah, this 00:13:03.309 --> 00:13:06.049 is key. First, avoid what we call the flat file 00:13:06.049 --> 00:13:08.190 fallacy. Flat file fallacy. That's just dumping 00:13:08.190 --> 00:13:10.870 absolutely everything into one giant sprawling 00:13:10.870 --> 00:13:13.389 document. Don't do that. OK, so what instead? 00:13:13.730 --> 00:13:16.409 Instead, embrace a modular design. Break your 00:13:16.409 --> 00:13:18.870 knowledge into smaller interlinked modules. Think 00:13:18.870 --> 00:13:21.600 of it like stacking Lego blocks of data. Lego 00:13:21.600 --> 00:13:23.379 blocks. I like that. You might have a company 00:13:23.379 --> 00:13:26.639 info .md file, a separate brand voice guide .md, 00:13:26.820 --> 00:13:29.080 maybe a solder dedicated to customer personas. 00:13:29.740 --> 00:13:32.799 Each one neatly categorized. And tagging documents 00:13:32.799 --> 00:13:35.500 with metadata seems smart too, like version 2 00:13:35.500 --> 00:13:39.200 .1 or audience. Internal. Absolutely. Metadata 00:13:39.200 --> 00:13:42.379 is key for organization and for retrieval later. 00:13:42.700 --> 00:13:45.500 Also include examples. Examples. Yeah. If you're 00:13:45.500 --> 00:13:47.399 providing a brand voice guide, don't just give 00:13:47.399 --> 00:13:50.639 it rules. Show it do and don't. Examples of actual 00:13:50.639 --> 00:13:53.139 sentences. Ah, practical examples. Makes sense. 00:13:53.299 --> 00:13:55.809 And critically... establish a process to keep 00:13:55.809 --> 00:13:58.649 it updated. Information changes, right? Your 00:13:58.649 --> 00:14:01.149 knowledge base has to be a living thing. You 00:14:01.149 --> 00:14:04.169 can even, in a kind of interesting twist, ask 00:14:04.169 --> 00:14:06.789 the AI itself how it would prefer you structure 00:14:06.789 --> 00:14:09.370 your knowledge files for it to consume them optimally. 00:14:09.690 --> 00:14:12.370 Really? Ask the AI. Yeah, why not? Get its input. 00:14:12.549 --> 00:14:15.269 So a well -structured modular knowledge base 00:14:15.269 --> 00:14:17.649 is really the foundation for getting intelligent, 00:14:17.909 --> 00:14:20.690 reliable AI outputs. Yes, absolutely. It's the 00:14:20.690 --> 00:14:23.529 bedrock for truly intelligent, reliable, and 00:14:23.340 --> 00:14:26.000 consistent AI performance. Okay, let's talk about 00:14:26.000 --> 00:14:28.779 some advanced strategies now. Advanced concept 00:14:28.779 --> 00:14:31.139 one, context chaining and refinement. This sounds 00:14:31.139 --> 00:14:33.139 like making the context itself smarter as it 00:14:33.139 --> 00:14:35.039 moves through a workflow. It is, essentially. 00:14:35.419 --> 00:14:38.960 Imagine a complex customer support process. Okay. 00:14:39.399 --> 00:14:43.639 Okay. Step one. An AI agent extracts key details 00:14:43.639 --> 00:14:46.039 and maybe the customer's sentiment from an incoming 00:14:46.039 --> 00:14:48.539 email. That refined context gets passed along. 00:14:48.960 --> 00:14:52.299 Right. Step two, a different AI agent uses, say, 00:14:52.600 --> 00:14:54.740 an order number from that context to query your 00:14:54.740 --> 00:14:57.039 internal database, pulling up purchase history. 00:14:57.200 --> 00:15:00.320 More context. Step three, another agent searches 00:15:00.320 --> 00:15:02.179 your help articles based on the issue identified 00:15:02.179 --> 00:15:05.039 earlier. Building context layer by layer. Exactly. 00:15:05.460 --> 00:15:08.700 Step four. All this refined context, the initial 00:15:08.700 --> 00:15:11.279 query, the order history, the relevant help articles, 00:15:11.600 --> 00:15:14.039 is then synthesized by maybe a final agent to 00:15:14.039 --> 00:15:16.500 draft a highly personalized and accurate reply. 00:15:16.980 --> 00:15:19.299 Each step dynamically refines the context for 00:15:19.299 --> 00:15:21.360 the next, building a richer and richer understanding. 00:15:21.559 --> 00:15:23.820 That's incredibly powerful. And then adaptive 00:15:23.820 --> 00:15:26.100 context systems or learning loops. This means 00:15:26.100 --> 00:15:29.080 the AI actually learns and gets better over time 00:15:29.080 --> 00:15:31.159 from your interaction. Perfectly. This involves 00:15:31.159 --> 00:15:33.519 building explicit feedback loops right into your 00:15:33.519 --> 00:15:35.940 AI workflows. How does that work? So if an AI 00:15:35.940 --> 00:15:38.399 generates a result, users can rate it. Excellent, 00:15:38.639 --> 00:15:42.139 acceptable, poor. Simple rating. Yeah. A poor 00:15:42.139 --> 00:15:44.559 rating, for instance, could automatically trigger 00:15:44.559 --> 00:15:47.820 a process. Maybe it saves that specific prompt 00:15:47.820 --> 00:15:50.460 and its corresponding context for a human to 00:15:50.460 --> 00:15:53.600 review later. So humans can check the mistakes. 00:15:53.720 --> 00:15:56.480 Exactly. This allows you to identify where the 00:15:56.480 --> 00:15:59.850 AI stumbled, Make corrections maybe to the knowledge 00:15:59.850 --> 00:16:02.789 base or the prompt and essentially train the 00:16:02.789 --> 00:16:05.669 system to improve future outputs based on real 00:16:05.669 --> 00:16:07.750 -world performance So it learns your preferences. 00:16:07.789 --> 00:16:09.610 It learns your preferences your business patterns 00:16:09.610 --> 00:16:12.820 and it truly adapts over time This all sounds 00:16:12.820 --> 00:16:15.679 incredibly powerful, almost revolutionary. But 00:16:15.679 --> 00:16:19.440 is context engineering a magic bullet? What can't 00:16:19.440 --> 00:16:22.240 it fix? Yeah, it's not a silver bullet, no. Hallucinations, 00:16:22.299 --> 00:16:24.860 while significantly reduced, can still happen, 00:16:25.379 --> 00:16:27.700 especially if the underlying data is flawed or 00:16:27.700 --> 00:16:30.159 the task is just inherently ambiguous. Garbage 00:16:30.159 --> 00:16:32.179 in, garbage out still applies. Fundamentally, 00:16:32.320 --> 00:16:36.019 yes. Poorly written, vague prompts will still 00:16:36.019 --> 00:16:38.799 yield poor results, even with excellent context. 00:16:39.340 --> 00:16:41.580 If the initial instruction isn't clear, context 00:16:41.580 --> 00:16:44.620 can only do so much. And, you know, it works 00:16:44.620 --> 00:16:47.059 within the basic limitations of the AI model 00:16:47.059 --> 00:16:49.600 itself. You can't make a model fundamentally 00:16:49.600 --> 00:16:52.639 smarter than its core architecture allows. But 00:16:52.639 --> 00:16:55.379 what does it dramatically improve for businesses 00:16:55.379 --> 00:16:59.399 using AI? today. Oh, it drastically reduces those 00:16:59.399 --> 00:17:02.519 frustrating hallucinations. It dramatically increases 00:17:02.519 --> 00:17:05.099 accuracy and personalization in the outputs. 00:17:05.220 --> 00:17:08.180 And consistency. And crucially enhances consistency 00:17:08.180 --> 00:17:11.440 across all your AI generated content and actions. 00:17:11.839 --> 00:17:13.740 So these advanced strategies like chaining and 00:17:13.740 --> 00:17:16.039 learning loops, they allow for iterative improvement 00:17:16.039 --> 00:17:19.480 and AI adaptation. Yes, making AI systems smarter, 00:17:19.900 --> 00:17:22.779 more robust, and much more adaptable over time. 00:17:22.880 --> 00:17:25.259 OK, so how does someone actually start implementing 00:17:25.259 --> 00:17:27.619 this today without feeling completely overwhelmed? 00:17:27.700 --> 00:17:29.759 What's the practical action plan? Right, let's 00:17:29.759 --> 00:17:31.740 break it down. The very first step is just to 00:17:31.740 --> 00:17:34.599 audit your current AI usage. Look where you're 00:17:34.599 --> 00:17:37.000 using it now. Yeah. Where are you consistently 00:17:37.000 --> 00:17:39.559 getting those generic responses? Where are you 00:17:39.559 --> 00:17:42.460 still manually copy -pasting information just 00:17:42.460 --> 00:17:45.599 to get the AI to understand? Identify those specific 00:17:45.599 --> 00:17:47.980 pain points first. Then choose your workflow 00:17:47.980 --> 00:17:50.259 tool. You mentioned a few earlier. Yeah. For 00:17:50.259 --> 00:17:52.400 those just starting out or who like things visual, 00:17:53.039 --> 00:17:55.779 no code platforms like Relay .app are fantastic. 00:17:56.079 --> 00:17:58.579 Really easy to get started. OK. If your needs 00:17:58.579 --> 00:18:01.940 are more complex, maybe require deep integrations 00:18:01.940 --> 00:18:04.539 across different business systems. Platforms 00:18:04.539 --> 00:18:07.279 like Mindpal offer that kind of enterprise grade 00:18:07.279 --> 00:18:10.920 power. And for the more technically inclined 00:18:10.920 --> 00:18:13.839 who want maximum control and customization, open 00:18:13.839 --> 00:18:16.940 source tools like N8n give you immense flexibility, 00:18:17.359 --> 00:18:19.630 though there's a steeper learning curve. And 00:18:19.630 --> 00:18:22.609 vitally, start simple. Please, start simple. 00:18:22.670 --> 00:18:25.170 Don't try to boil the ocean. Exactly. Pick just 00:18:25.170 --> 00:18:27.509 one workflow that addresses a specific, measurable 00:18:27.509 --> 00:18:30.190 pain point. Maybe it's those sales follow -up 00:18:30.190 --> 00:18:33.750 emails, or internal social media posts, or streamlining 00:18:33.750 --> 00:18:35.849 customer support responses. Focus on one thing 00:18:35.849 --> 00:18:38.769 first. Yes. Resist the urge to automate everything 00:18:38.769 --> 00:18:41.700 at once. Mastery comes from iterative improvement 00:18:41.700 --> 00:18:43.880 on small, high -impact processes. Then build 00:18:43.880 --> 00:18:46.220 your properly structured, modular knowledge base, 00:18:46.480 --> 00:18:49.059 like we discussed. That organized information 00:18:49.059 --> 00:18:52.160 is key. Absolutely. Don't underestimate the power 00:18:52.160 --> 00:18:55.319 of clear, organized information for the AI. Then 00:18:55.319 --> 00:18:58.000 test and iterate, constantly. It's not set and 00:18:58.000 --> 00:19:00.900 forget. Definitely not. Adjusting context, refining 00:19:00.900 --> 00:19:03.740 your knowledge files, fine -tuning those contextual 00:19:03.740 --> 00:19:06.440 prompts. It's an ongoing process. And finally, 00:19:06.859 --> 00:19:09.700 scale gradually. OK. Once you have one successful 00:19:09.700 --> 00:19:12.420 workflow running smoothly, then add more context 00:19:12.420 --> 00:19:15.240 sources, create new workflows, and expand its 00:19:15.240 --> 00:19:17.579 reach within your operations. This really feels 00:19:17.579 --> 00:19:19.920 like the inevitable direction for AI in business, 00:19:20.059 --> 00:19:22.220 doesn't it? Moving from AI is just a helpful 00:19:22.220 --> 00:19:25.859 tool to AI as a true team member. I think so. 00:19:26.970 --> 00:19:29.730 Context engineering in this light is like the 00:19:29.730 --> 00:19:32.710 essential onboarding and ongoing management process 00:19:32.710 --> 00:19:36.069 for this new, incredibly powerful team member. 00:19:36.089 --> 00:19:37.990 That's a great way to put it. Businesses mastering 00:19:37.990 --> 00:19:41.170 this now, they'll undoubtedly have a significant 00:19:41.170 --> 00:19:43.009 competitive advantage, wouldn't you say? Oh, 00:19:43.009 --> 00:19:45.670 absolutely. The single biggest takeaway, I think, 00:19:45.769 --> 00:19:48.029 from all this for our listeners is that context 00:19:48.029 --> 00:19:50.269 engineering represents a fundamental shift in 00:19:50.269 --> 00:19:52.869 how we work with AI. A shift in thinking. Yes. 00:19:53.250 --> 00:19:56.190 It's the key to enabling consistent, high quality 00:19:56.079 --> 00:19:59.440 and deeply personalized outputs by creating these 00:19:59.440 --> 00:20:02.180 complete intelligent informational environments 00:20:02.180 --> 00:20:05.380 for your AI. So starting small with context engineering 00:20:05.380 --> 00:20:08.180 is really the path to unlocking AI's full potential 00:20:08.180 --> 00:20:11.029 as a true business partner. Exactly. It's the 00:20:11.029 --> 00:20:13.789 pathway to deeply integrated and truly understanding 00:20:13.789 --> 00:20:16.750 AI systems within your operations. So wrapping 00:20:16.750 --> 00:20:20.049 up our big idea today, context engineering is 00:20:20.049 --> 00:20:22.369 far more than just another buzzword. Much more. 00:20:22.650 --> 00:20:25.609 It's the essential next level evolution for unlocking 00:20:25.609 --> 00:20:28.509 AI's true transformative potential within your 00:20:28.509 --> 00:20:31.650 business. It's about moving beyond isolated one 00:20:31.650 --> 00:20:35.000 -off prompts. To building a rich. dynamic and 00:20:35.000 --> 00:20:37.079 continuously evolving informational environment 00:20:37.079 --> 00:20:41.059 for your AI. That's the core idea. And by diligently 00:20:41.059 --> 00:20:44.059 applying that real framework, making sure your 00:20:44.059 --> 00:20:47.450 context is always relevant, efficient. accessible, 00:20:47.450 --> 00:20:50.650 and logical. And by properly structuring your 00:20:50.650 --> 00:20:52.789 AI knowledge bases with that modular design we 00:20:52.789 --> 00:20:54.829 talked about. The Lego blocks. The Lego blocks. 00:20:55.009 --> 00:20:57.430 You can transform those generic AI outputs into 00:20:57.430 --> 00:21:00.390 high quality, personalized, and remarkably consistent 00:21:00.390 --> 00:21:02.930 results. This isn't just about efficiency, though. 00:21:02.970 --> 00:21:04.970 It's about gaining a serious competitive edge. 00:21:05.210 --> 00:21:07.390 So if you're ready to dive in to put this into 00:21:07.390 --> 00:21:10.470 practice, the advice is start simple. Please 00:21:10.470 --> 00:21:13.130 do. choose just one process in your business 00:21:13.130 --> 00:21:15.049 where you're currently getting those kind of 00:21:15.049 --> 00:21:18.089 generic AI responses and apply the real framework 00:21:18.089 --> 00:21:21.230 to it. Connect your AI to live data sources, 00:21:21.930 --> 00:21:24.190 structure your knowledge files logically, and 00:21:24.190 --> 00:21:26.990 then just observe the transformation, see what 00:21:26.990 --> 00:21:29.009 happens. The time you invest in learning these 00:21:29.009 --> 00:21:32.230 concepts now. It's really going to pay immense 00:21:32.230 --> 00:21:35.109 dividends as AI becomes increasingly central 00:21:35.109 --> 00:21:37.349 to pretty much every facet of business operations. 00:21:37.349 --> 00:21:39.809 It feels inevitable. It really does. It's about 00:21:39.809 --> 00:21:42.650 building AI systems that truly understand your 00:21:42.650 --> 00:21:45.390 business, your customers, and your goals at a 00:21:45.390 --> 00:21:47.839 much deeper level. And for you, our listener, 00:21:48.059 --> 00:21:51.079 here's maybe a final thought to mull over. Consider 00:21:51.079 --> 00:21:54.140 how treating AI as a true team member, requiring 00:21:54.140 --> 00:21:56.680 that proper onboarding and ongoing management 00:21:56.680 --> 00:21:59.519 via context, how does that fundamentally change 00:21:59.519 --> 00:22:02.599 your approach to automation? What new possibilities 00:22:02.599 --> 00:22:05.259 emerge when AI genuinely understands your business 00:22:05.259 --> 00:22:08.039 in this deep, integrated way? Something to think 00:22:08.039 --> 00:22:10.119 about. That's all for this deep dive. Thanks 00:22:10.119 --> 00:22:11.839 for joining us. Thanks for having me. We'll be 00:22:11.839 --> 00:22:14.700 back soon with more fascinating insights. Out 00:22:14.700 --> 00:22:15.240 of your music.