WEBVTT 00:00:00.000 --> 00:00:03.339 Imagine an AI customer service agent. You know, 00:00:03.339 --> 00:00:05.080 it's brilliant at first, handles everything perfectly, 00:00:05.080 --> 00:00:08.419 but then, maybe a few weeks in, it starts. Well, 00:00:08.500 --> 00:00:10.619 veering off course. Maybe it offers a discount 00:00:10.619 --> 00:00:14.119 that just doesn't exist, or it asks you for your 00:00:14.119 --> 00:00:16.640 customer ID for the third time in the same chat. 00:00:16.980 --> 00:00:19.600 Customers get frustrated, naturally, in your 00:00:19.600 --> 00:00:22.500 business. Well, it starts to bleed money. The 00:00:22.500 --> 00:00:25.300 core problem here isn't just like a bad prompt 00:00:25.300 --> 00:00:27.320 someone wrote. It's something much more subtle. 00:00:27.480 --> 00:00:30.260 It's context degradation. Welcome to the deep 00:00:30.260 --> 00:00:32.200 dive. Today we're taking a really deep plunge 00:00:32.200 --> 00:00:35.359 into context engineering, mastering AI information 00:00:35.359 --> 00:00:37.740 flow. And this is just about you know, tweaking 00:00:37.740 --> 00:00:39.560 a command here or there. It's really a whole 00:00:39.560 --> 00:00:42.100 new way of thinking about AI, how it processes 00:00:42.100 --> 00:00:45.420 information, and ultimately how effective it 00:00:45.420 --> 00:00:47.280 can truly be. That's exactly right. We're going 00:00:47.280 --> 00:00:51.219 to unpack why the traditional way prompt engineering, 00:00:51.299 --> 00:00:53.200 while it's still important, it just isn't enough 00:00:53.200 --> 00:00:56.780 anymore, not with today's complex AI. And then, 00:00:56.780 --> 00:00:59.920 yeah, we'll explore nine really practical strategies, 00:01:00.420 --> 00:01:02.799 ways to build what we're calling an informational 00:01:02.799 --> 00:01:05.560 nervous system for AI. These are the techniques 00:01:05.560 --> 00:01:08.810 that, you know, really lift AI agents up, make 00:01:08.810 --> 00:01:11.250 them intelligent and reliable collaborators. 00:01:11.730 --> 00:01:13.409 Okay, let's unpack this, and I'm really curious 00:01:13.409 --> 00:01:15.370 to understand this fundamental shift you're talking 00:01:15.370 --> 00:01:17.909 about. It does feel like the whole landscape 00:01:17.909 --> 00:01:21.310 of interacting with AI is changing pretty fast. 00:01:21.930 --> 00:01:23.810 We're definitely moving beyond just, you know... 00:01:23.680 --> 00:01:26.500 write a good prompt and cross your fingers. Absolutely. 00:01:26.959 --> 00:01:28.900 Context engineering, it's not just some fancy 00:01:28.900 --> 00:01:30.959 term. It's really about deliberately designing, 00:01:31.219 --> 00:01:34.739 managing, and continuously optimizing that stream 00:01:34.739 --> 00:01:37.239 of information, what the AI agent gets and what 00:01:37.239 --> 00:01:39.299 it remembers over time. Think of it like building 00:01:39.299 --> 00:01:41.000 that informational nervous system we mentioned. 00:01:41.319 --> 00:01:43.400 It's kind of like overseeing a really complex 00:01:43.400 --> 00:01:45.480 conversation, maybe multi -layered, where you 00:01:45.480 --> 00:01:47.560 constantly have to track what's been said, what's 00:01:47.560 --> 00:01:49.359 crucial to keep, and what's just noise you can 00:01:49.359 --> 00:01:52.709 discard. So why now? What's really driving this 00:01:52.709 --> 00:01:55.609 shift right now? Why is context suddenly so critical? 00:01:56.250 --> 00:01:58.989 Well, there's a great analogy from André Carpathy, 00:01:59.230 --> 00:02:02.069 a leading AI researcher. He compares large language 00:02:02.069 --> 00:02:04.609 models, these LLMs, that understand and generate 00:02:04.609 --> 00:02:07.609 text like humans. He compares them to a new kind 00:02:07.609 --> 00:02:10.340 of operating system. And that critical space 00:02:10.340 --> 00:02:13.039 where the AI holds all its current info, the 00:02:13.039 --> 00:02:15.560 context window, that's its RAM, its limited working 00:02:15.560 --> 00:02:18.560 memory, everything the AI needs to think to act 00:02:18.560 --> 00:02:20.479 effectively has to fit right in there. Okay, 00:02:20.900 --> 00:02:22.699 and just like with our computers, that RAM can 00:02:22.699 --> 00:02:26.330 get messy. Really fast. Exactly. And that messiness, 00:02:26.330 --> 00:02:29.289 it leads to some pretty significant issues. And 00:02:29.289 --> 00:02:31.169 these aren't just technical glitches. They can 00:02:31.169 --> 00:02:33.389 become real business headaches. First, you've 00:02:33.389 --> 00:02:35.930 got context poisoning. This is when wrong information 00:02:35.930 --> 00:02:38.289 gets kind of stuck in the AI's memory. Like, 00:02:38.490 --> 00:02:40.849 imagine a sales agent. Just once. It gets hold 00:02:40.849 --> 00:02:43.689 of an unofficial 20 % discount figure. If it 00:02:43.689 --> 00:02:45.729 saves that nugget, it might just keep offering 00:02:45.729 --> 00:02:48.550 that wrong discount. over and over to every customer. 00:02:48.969 --> 00:02:51.229 That causes direct revenue loss until someone 00:02:51.229 --> 00:02:53.629 catches it. It's pretty insidious. Wow, so a 00:02:53.629 --> 00:02:55.729 single bad piece of data can really snowball 00:02:55.729 --> 00:02:58.669 like that. It really can. Then there's context 00:02:58.669 --> 00:03:01.080 distraction. This happens when there's just too 00:03:01.080 --> 00:03:03.539 much irrelevant info. Creates this excessive 00:03:03.539 --> 00:03:06.000 cognitive load for the AI. It's like you trying 00:03:06.000 --> 00:03:07.639 to listen to five different conversations at 00:03:07.639 --> 00:03:10.080 once in a crowded room. The AI gets overwhelmed, 00:03:10.259 --> 00:03:12.219 it struggles to focus on its main job, and often 00:03:12.219 --> 00:03:15.060 it just defaults to generic kind of off -topic 00:03:15.060 --> 00:03:16.699 answers. It can't figure out what's actually 00:03:16.699 --> 00:03:20.550 important. And classic one. the needle in a haystack 00:03:20.550 --> 00:03:23.069 problem, sometimes called lost in the middle. 00:03:24.289 --> 00:03:26.689 Research shows this again and again. LLMs often 00:03:26.689 --> 00:03:28.569 ignore crucial details if they're buried in the 00:03:28.569 --> 00:03:30.430 middle of a long text. They pay more attention 00:03:30.430 --> 00:03:32.830 to the beginning and the end. So your most vital 00:03:32.830 --> 00:03:35.129 piece of info could be right there, but the AI 00:03:35.129 --> 00:03:38.509 just breezes past it. Like burying the lead, 00:03:39.069 --> 00:03:42.590 but for an AI. Precisely. And finally, there's 00:03:42.590 --> 00:03:45.729 context drift. So in long -running tasks, the 00:03:45.729 --> 00:03:47.590 original goal can just completely fade away. 00:03:48.140 --> 00:03:51.219 Picture an agent, right? Its main task is drafting 00:03:51.219 --> 00:03:54.000 a comprehensive product launch plan. But maybe 00:03:54.000 --> 00:03:56.300 the user keeps interrupting, asking it to check 00:03:56.300 --> 00:03:58.879 emails or summarize some unrelated articles. 00:03:59.340 --> 00:04:01.659 The context window fills up with these side quests, 00:04:02.039 --> 00:04:03.919 and eventually the agent just loses sight of 00:04:03.919 --> 00:04:05.759 its main mission, forgets what it was supposed 00:04:05.759 --> 00:04:08.159 to be doing. And on top of all these performance 00:04:08.159 --> 00:04:10.719 issues, there's a direct economic hit, too, isn't 00:04:10.719 --> 00:04:12.460 there? We're talking actual dollars and cents. 00:04:12.960 --> 00:04:14.819 Absolutely. Every single token, which is basically 00:04:14.819 --> 00:04:17.699 a piece of a word or even a character, that gets 00:04:17.699 --> 00:04:20.600 fed into that context window costs API fees. 00:04:20.980 --> 00:04:23.259 So an inefficient context management system, 00:04:23.480 --> 00:04:25.759 it will quite literally burn your money just 00:04:25.759 --> 00:04:28.160 processing junk information. And that adds up 00:04:28.160 --> 00:04:30.379 incredibly quickly when you scale. So these aren't 00:04:30.379 --> 00:04:32.379 just minor glitches then. They're symptoms of 00:04:32.379 --> 00:04:34.879 something deeper. It shows that a perfect prompt 00:04:35.160 --> 00:04:37.899 just isn't the whole story anymore, right? Right. 00:04:38.019 --> 00:04:41.100 Exactly. It clearly signals that AI needs a much 00:04:41.100 --> 00:04:44.639 more sophisticated intelligence system for managing 00:04:44.639 --> 00:04:47.180 context if it's going to work effectively over 00:04:47.180 --> 00:04:49.420 time. It goes way beyond just a single perfect 00:04:49.420 --> 00:04:51.579 command. It's not just about what you tell it, 00:04:51.579 --> 00:04:53.560 but what the AI actually hears and remembers 00:04:53.560 --> 00:04:56.300 and keeps track of. Okay, so if we need this 00:04:56.300 --> 00:04:58.920 intelligence system, what are some of these strategies 00:04:58.920 --> 00:05:00.699 for actually building it? Let's start with memory. 00:05:00.759 --> 00:05:03.279 That feels pretty foundational. For sure. First 00:05:03.279 --> 00:05:06.310 up is short -term memory. This is like fundamental 00:05:06.310 --> 00:05:09.069 for any real conversation, right? It lets the 00:05:09.069 --> 00:05:11.550 AI remember what just happened, the recent interactions. 00:05:12.110 --> 00:05:14.370 Most AI platforms let you configure this pretty 00:05:14.370 --> 00:05:16.610 easily. You usually just set a number of recent 00:05:16.610 --> 00:05:19.050 messages to keep in mind. It's low latency, meaning 00:05:19.050 --> 00:05:21.709 it's fast and pretty simple to set up. So if 00:05:21.709 --> 00:05:26.509 a user says, my customer ID is KH8675, the AI 00:05:26.509 --> 00:05:28.230 holds onto that for the next question, like, 00:05:28.269 --> 00:05:30.529 what's my current balance? just a moment later. 00:05:31.129 --> 00:05:34.129 Now, for more granular control, you could use 00:05:34.129 --> 00:05:36.110 an external database, something like Postgresql. 00:05:36.389 --> 00:05:38.629 That gives you persistence across sessions, plus 00:05:38.629 --> 00:05:41.370 you can query it robustly. You can design a really 00:05:41.370 --> 00:05:43.769 clear message table structure. That helps a lot 00:05:43.769 --> 00:05:45.790 with debugging and analysis later on, though 00:05:45.790 --> 00:05:48.170 it does add a little bit more latency. So it's 00:05:48.170 --> 00:05:50.550 not just about speed. It's really about the fluidity 00:05:50.550 --> 00:05:53.189 of the interaction itself. Without that basic 00:05:53.189 --> 00:05:56.329 recall, every AI chat would feel like talking 00:05:56.329 --> 00:05:58.680 to someone with severe amnesia. Just repeating 00:05:58.680 --> 00:06:00.540 yourself over and over. That's a great way to 00:06:00.540 --> 00:06:04.199 put it, yeah. Frustratingly repetitive. Then 00:06:04.199 --> 00:06:06.180 there's the idea of more durable intelligence, 00:06:06.399 --> 00:06:09.000 which brings us to long -term memory. This is 00:06:09.000 --> 00:06:10.920 what keeps critical information handy across 00:06:10.920 --> 00:06:13.120 multiple sessions, maybe even ones that are far 00:06:13.120 --> 00:06:16.060 apart in time. A simple approach. Maybe just 00:06:16.060 --> 00:06:18.879 a text file, or even something like Google Docs. 00:06:19.019 --> 00:06:21.860 But more robust systems actually classify memory 00:06:21.860 --> 00:06:24.680 types, helps keep things organized. So you might 00:06:24.680 --> 00:06:27.199 have user memory for individual preferences or 00:06:27.199 --> 00:06:29.920 interaction history like, this user prefers reports 00:06:29.920 --> 00:06:32.480 on Monday mornings. Then maybe domain memory 00:06:32.480 --> 00:06:34.819 for business rules, product info, stuff like, 00:06:34.980 --> 00:06:38.649 our standard return policy is 30 days. And finally, 00:06:38.970 --> 00:06:41.290 task memory. This tracks the state of longer 00:06:41.290 --> 00:06:43.350 projects like we're currently in the planning 00:06:43.350 --> 00:06:45.790 phase of Project X. Okay, but how does an AI 00:06:45.790 --> 00:06:47.949 know when to forget things or when information 00:06:47.949 --> 00:06:50.110 gets outdated? That seems like a really tricky 00:06:50.110 --> 00:06:52.350 challenge for long -term memory especially. Oh, 00:06:52.449 --> 00:06:55.589 it absolutely is. That's a critical point. Unlike 00:06:55.589 --> 00:06:58.769 us humans, AI doesn't just intuitively forget 00:06:58.769 --> 00:07:01.850 stuff that's no longer relevant. It needs periodic 00:07:01.850 --> 00:07:04.949 memory validation, active cleanup mechanisms. 00:07:05.129 --> 00:07:07.149 Maybe you have an automated process that runs, 00:07:07.149 --> 00:07:09.689 say, nightly to check and refresh the agent's 00:07:09.689 --> 00:07:11.829 memories. This is a key area for active management. 00:07:11.930 --> 00:07:13.970 Otherwise, you risk that context poisoning we 00:07:13.970 --> 00:07:16.310 talked about earlier. Right, right. And tools, 00:07:16.310 --> 00:07:18.670 they add so much power to AI, but they also seem 00:07:18.670 --> 00:07:20.769 like they add another whole layer of complexity 00:07:20.769 --> 00:07:23.529 to this context management puzzle. They definitely 00:07:23.529 --> 00:07:25.709 do. And that brings us to our third strategy, 00:07:26.230 --> 00:07:29.209 context from tool calling. How you describe your 00:07:29.209 --> 00:07:33.170 tools to the AI profoundly influences its decisions 00:07:33.170 --> 00:07:35.350 about when and how to use them. A really poor 00:07:35.350 --> 00:07:37.170 description, like just calling something search 00:07:37.170 --> 00:07:39.629 tool, gives the AI very little guidance. It's 00:07:39.629 --> 00:07:42.550 vague. But a good description, something like 00:07:42.550 --> 00:07:45.970 product information search tool. Use search product. 00:07:46.189 --> 00:07:49.589 product name dot string. Use this specific tool 00:07:49.589 --> 00:07:52.149 to look up details, pricing, and stop status 00:07:52.149 --> 00:07:54.430 for a specific product. See, that's much more 00:07:54.430 --> 00:07:57.709 effective. It tells the AI exactly when and how 00:07:57.709 --> 00:07:59.990 to use that tool. It's almost like baking the 00:07:59.990 --> 00:08:02.230 instruction manual right into its memory. And 00:08:02.230 --> 00:08:04.730 when you start chaining tools together, where 00:08:04.730 --> 00:08:07.170 one tool's output feeds into the next one context, 00:08:07.329 --> 00:08:09.779 it gets incredibly complex, really fast. You 00:08:09.779 --> 00:08:12.379 absolutely need strict isolation and summarization 00:08:12.379 --> 00:08:14.860 strategies there just to prevent cross contamination 00:08:14.860 --> 00:08:17.279 between the steps. So it sounds like AI memory 00:08:17.279 --> 00:08:19.910 isn't like ours at all. It needs constant active 00:08:19.910 --> 00:08:21.569 gardening, you could say, it doesn't naturally 00:08:21.569 --> 00:08:23.370 prune itself. Is that the gist? That's exactly 00:08:23.370 --> 00:08:25.870 it. It needs active, intelligent management because 00:08:25.870 --> 00:08:28.269 it doesn't inherently filter or forget like we 00:08:28.269 --> 00:08:31.310 do. OK, let's shift gears a bit. How do we give 00:08:31.310 --> 00:08:34.049 AI access to truly vast amounts of knowledge, 00:08:34.629 --> 00:08:37.289 like huge databases or document sets, without 00:08:37.289 --> 00:08:39.529 completely overwhelming its limited working memory? 00:08:39.610 --> 00:08:41.669 That seems like a massive challenge. Yeah, and 00:08:41.669 --> 00:08:43.590 that brings us nicely to our fourth strategy, 00:08:43.889 --> 00:08:46.929 ARAG Retrieval Augmented Generation. This is 00:08:46.929 --> 00:08:48.429 a really elegant solution, actually. It allows 00:08:48.429 --> 00:08:51.490 AI agents to. dynamically pull in information 00:08:51.490 --> 00:08:53.950 from large external knowledge bases just when 00:08:53.950 --> 00:08:56.389 they need it. Now, to make CAR -RAC work well, 00:08:56.409 --> 00:08:58.450 you really need to think about a few key components. 00:08:58.730 --> 00:09:01.730 First, your chunking strategy. How you break 00:09:01.730 --> 00:09:03.809 up your knowledge base. Don't just split documents 00:09:03.809 --> 00:09:07.070 by a fixed size, like every 500 words. Use more 00:09:07.070 --> 00:09:09.409 advanced methods, like recursive chunking or 00:09:09.409 --> 00:09:12.269 semantic chunking. These try to group related 00:09:12.269 --> 00:09:15.090 sentences or ideas together, creating more coherent 00:09:15.090 --> 00:09:18.129 text segments for the AI to understand. Next, 00:09:18.389 --> 00:09:20.070 choosing and embedding model. This is crucial. 00:09:20.190 --> 00:09:22.009 This model is what translates your text into 00:09:22.009 --> 00:09:24.230 a sort of mathematical representation that the 00:09:24.230 --> 00:09:27.210 AI uses for similarity searches. Picking the 00:09:27.210 --> 00:09:30.070 right one is key for finding relevant info. Then, 00:09:30.149 --> 00:09:32.110 advanced retrieval techniques. You want to combine 00:09:32.110 --> 00:09:34.070 traditional keyword search, like the old -school 00:09:34.070 --> 00:09:37.090 BM -25 method, good for finding exact word matches 00:09:37.090 --> 00:09:39.190 with semantic search, which understands meaning 00:09:39.190 --> 00:09:41.909 and concepts. This combo, often called hybrid 00:09:41.909 --> 00:09:44.269 search, usually gives you much more relevant 00:09:44.269 --> 00:09:46.450 results, especially for tricky technical terms 00:09:46.450 --> 00:09:50.090 or nuanced ideas. Finally, re -ranking. After 00:09:50.090 --> 00:09:52.289 you retrieve, say, the top 10 potential documents, 00:09:52.730 --> 00:09:55.450 you use a smaller, faster model to re -evaluate 00:09:55.450 --> 00:09:57.850 just those top hits. It sorts them again based 00:09:57.850 --> 00:10:00.629 on relevance to the specific query, pushing the 00:10:00.629 --> 00:10:02.450 absolute most critical information right to the 00:10:02.450 --> 00:10:04.909 very top for the main powerful LLM to process 00:10:04.909 --> 00:10:07.710 first. Can you give us a quick, concrete example 00:10:07.710 --> 00:10:09.850 of that re -ranking? How does that work in practice? 00:10:10.090 --> 00:10:13.009 Sure. Let's say someone asks, what's the warranty 00:10:13.009 --> 00:10:17.190 policy for the X15 laptop? Hybrid search would 00:10:17.190 --> 00:10:20.330 pull documents containing the keyword X15 and 00:10:20.330 --> 00:10:22.990 also documents semantically related to warranty 00:10:22.990 --> 00:10:25.990 policy. The re -ranker then looks at those results 00:10:25.990 --> 00:10:28.190 and says, okay, this specific passage mentions 00:10:28.190 --> 00:10:32.049 both X15 and warranty policy very clearly. It 00:10:32.049 --> 00:10:34.629 then pushes that exact passage to the number 00:10:34.629 --> 00:10:37.370 one spot in the context window. This ensures 00:10:37.370 --> 00:10:40.509 the main LLM sees the most relevant snippet immediately. 00:10:40.830 --> 00:10:42.669 Gotcha. That makes sense. Now, what about really 00:10:42.669 --> 00:10:44.570 complex tasks, things with multiple steps, maybe 00:10:44.570 --> 00:10:46.669 over a long period? How do you manage context 00:10:46.669 --> 00:10:49.990 there without it just drifting off course completely? 00:10:50.220 --> 00:10:52.779 Yeah, for those really complex scenarios, we 00:10:52.779 --> 00:10:55.820 turn to strategy number five, context isolation 00:10:55.820 --> 00:10:59.480 using a multi -agent approach. For complex tasks, 00:10:59.580 --> 00:11:01.879 often the best solution is to build a sort of 00:11:01.879 --> 00:11:05.039 hierarchical team of specialized AI agents. Think 00:11:05.039 --> 00:11:07.000 of it like a company's org chart. You might have 00:11:07.000 --> 00:11:09.600 a coordinator agent acting as the overall manager, 00:11:10.059 --> 00:11:12.919 then supervisor agents like team leads, and then 00:11:12.919 --> 00:11:14.919 worker agents as the specialists, each focused 00:11:14.919 --> 00:11:17.480 on one specific thing. Let's take an automated 00:11:17.480 --> 00:11:20.210 marketing team example. A coordinator agent gets 00:11:20.210 --> 00:11:22.409 the high -level goal, something like, increase 00:11:22.409 --> 00:11:25.350 brand awareness for product Y. It then delegates 00:11:25.350 --> 00:11:27.750 parts of this big goal. A content supervisor 00:11:27.750 --> 00:11:30.429 agent manages the overall content strategy. And 00:11:30.429 --> 00:11:32.149 under that supervisor, you might have a worker 00:11:32.149 --> 00:11:35.029 agent research. Its only job is keyword research 00:11:35.029 --> 00:11:37.070 and competitor analysis. It deals with all the 00:11:37.070 --> 00:11:39.629 messy web data, scraping pages, et cetera. But 00:11:39.629 --> 00:11:42.129 crucially, it doesn't pass that mess along. It 00:11:42.129 --> 00:11:44.570 processes it and returns only a clean, structured 00:11:44.570 --> 00:11:47.490 JSON file with its findings. Then a separate 00:11:47.490 --> 00:11:49.909 worker agent writing receives that clean JSON 00:11:49.909 --> 00:11:53.090 data. Its context is pure. No HTML noise, no 00:11:53.090 --> 00:11:55.009 irrelevant stuff from the web scraping. It just 00:11:55.009 --> 00:11:56.850 focuses on writing based on the structured data. 00:11:57.009 --> 00:12:00.029 Whoa. Okay. Imagine scaling that kind of setup 00:12:00.029 --> 00:12:04.309 to handle like a billion queries. The efficiency 00:12:04.309 --> 00:12:06.549 gain could be massive. That's a really powerful 00:12:06.549 --> 00:12:09.649 concept. But what are the biggest hurdles in 00:12:09.649 --> 00:12:12.210 actually implementing a multi -agent system like 00:12:12.210 --> 00:12:15.039 that? Seems complex. Yeah. The primary challenge 00:12:15.039 --> 00:12:17.320 is really designing those clear interfaces and 00:12:17.320 --> 00:12:19.919 communication protocols between the agents. Each 00:12:19.919 --> 00:12:21.899 agent needs to know precisely what information 00:12:21.899 --> 00:12:24.519 it expects as input and exactly what format its 00:12:24.519 --> 00:12:26.700 output should be in for the next agent down the 00:12:26.700 --> 00:12:29.039 line. It takes careful architectural planning 00:12:29.039 --> 00:12:31.460 up front to make it work smoothly. So whether 00:12:31.460 --> 00:12:34.200 it's breaking down tasks with agents or using 00:12:34.200 --> 00:12:37.379 RG, is the core idea basically just giving the 00:12:37.379 --> 00:12:40.620 AI only the information it needs for the immediate 00:12:40.620 --> 00:12:42.639 task, preventing it from getting overwhelmed 00:12:42.639 --> 00:12:44.500 or distracted. Precisely. That's the essence 00:12:44.500 --> 00:12:46.480 of it. Deliver only the most relevant pieces 00:12:46.480 --> 00:12:48.879 of information right when needed. It prevents 00:12:48.879 --> 00:12:51.059 distraction and dramatically improves focus. 00:12:51.240 --> 00:12:53.779 Okay. We've covered memory, how AI uses tools, 00:12:53.980 --> 00:12:56.580 even setting up AI teams. What else is in the 00:12:56.580 --> 00:12:58.679 toolkit for managing this critical information 00:12:58.679 --> 00:13:01.360 flow and keeping it clean and effective? Right. 00:13:01.460 --> 00:13:04.759 Next up is context summarization. This is all 00:13:04.759 --> 00:13:07.120 about the intelligent compression of information. 00:13:07.539 --> 00:13:09.779 It's not just shortening, it's making it dense 00:13:09.779 --> 00:13:12.580 with meaning. This usually breaks down into two 00:13:12.580 --> 00:13:16.039 main types. First, extractive summarization. 00:13:16.700 --> 00:13:18.879 This method simply pulls out the most important 00:13:18.879 --> 00:13:21.299 sentences directly from the original text. It's 00:13:21.299 --> 00:13:23.460 generally fast and pretty factual because it's 00:13:23.460 --> 00:13:26.360 just selecting existing phrases. So from a long 00:13:26.360 --> 00:13:28.899 email thread, it might pull out, customer reported 00:13:28.899 --> 00:13:32.450 error x and we suggested solution y. Straightforward. 00:13:32.990 --> 00:13:35.610 Then there's abstractive summarization. Here, 00:13:35.789 --> 00:13:38.750 the AI actually generates new sentences to capture 00:13:38.750 --> 00:13:41.169 the essence of the content. This often sounds 00:13:41.169 --> 00:13:43.990 more natural, more concise. But, and this is 00:13:43.990 --> 00:13:46.470 important, it carries a higher risk of hallucination, 00:13:46.570 --> 00:13:48.529 where the AI might accidentally make up details 00:13:48.529 --> 00:13:51.190 if you don't control it carefully. An abstractive 00:13:51.190 --> 00:13:52.990 summary of that same email thread might sound 00:13:52.990 --> 00:13:55.129 like. The customer encountered error X and the 00:13:55.129 --> 00:13:56.929 team successfully guided them through implementing 00:13:56.929 --> 00:13:59.570 solution Y. More fluid, but potentially less 00:13:59.570 --> 00:14:02.009 precise if not done well. And for those really 00:14:02.009 --> 00:14:04.730 complex workflows that need tight control, predictability, 00:14:04.970 --> 00:14:07.690 moving step by step. Ugh. For those, we often 00:14:07.690 --> 00:14:10.250 use strategy number seven, context -aware routing 00:14:10.250 --> 00:14:12.789 and staging. We actually borrow a concept from 00:14:12.789 --> 00:14:15.129 computer science here called a finite state machine, 00:14:15.409 --> 00:14:19.269 or FSM. Basically, each defined state in your 00:14:19.269 --> 00:14:22.090 workflow, like say, new order or payment pending 00:14:22.090 --> 00:14:24.950 or inventory check, has its own clearly defined 00:14:24.950 --> 00:14:28.129 context requirements and specific rules for transitioning 00:14:28.129 --> 00:14:30.500 to the next state. Think about processing an 00:14:30.500 --> 00:14:32.700 online order. The new order state just contains 00:14:32.700 --> 00:14:35.519 the basic order details. Agent A validates it. 00:14:35.620 --> 00:14:37.600 If it's good, it transitions to the inventory 00:14:37.600 --> 00:14:40.340 check state. In that state, Agent B calls the 00:14:40.340 --> 00:14:42.960 warehouse API tool with the product ID. If there's 00:14:42.960 --> 00:14:45.299 enough stock, it moves to payment pending. If 00:14:45.299 --> 00:14:47.059 not, maybe it goes to an out -of -stock state. 00:14:47.419 --> 00:14:49.840 This creates simple, predictable steps. It ensures 00:14:49.840 --> 00:14:52.240 the AI agent always has exactly the right context 00:14:52.240 --> 00:14:54.279 for its current specific task within the larger 00:14:54.279 --> 00:15:00.379 workflow. important too. Like, how does it prefer 00:15:00.379 --> 00:15:02.580 its data served up? Is there an optimal format? 00:15:02.960 --> 00:15:05.820 Absolutely crucial. And that brings us to strategy 00:15:05.820 --> 00:15:09.129 number eight. Context formatting. Especially 00:15:09.129 --> 00:15:11.090 when you're dealing with structured data coming 00:15:11.090 --> 00:15:14.250 from APIs or databases, just feeding it to the 00:15:14.250 --> 00:15:17.370 AI as like a natural language sentence is often 00:15:17.370 --> 00:15:19.889 really inefficient and surprisingly prone to 00:15:19.889 --> 00:15:22.289 errors. Instead, you should provide that data 00:15:22.289 --> 00:15:25.990 as clean, well -typed JSON. That's that standard 00:15:25.990 --> 00:15:28.330 text -based format for representing structured 00:15:28.330 --> 00:15:30.570 data. For example, instead of saying the product 00:15:30.570 --> 00:15:33.549 is a cotton t -shirt, it costs 250 ,000 VND, 00:15:33.690 --> 00:15:35.830 and they're 50 in stock at warehouse A, you'd 00:15:35.830 --> 00:15:38.049 use a JSON. something like product name, cotton 00:15:38.049 --> 00:15:41.429 t -shirt, price, 250 ,000, currency, VND, stock, 00:15:41.590 --> 00:15:44.809 quantity, 50, location, warehouse A. The LLM 00:15:44.809 --> 00:15:47.049 can parse and extract information from that structure 00:15:47.049 --> 00:15:50.169 format with much, much higher accuracy. It almost 00:15:50.169 --> 00:15:51.769 optimizes its own efficiency of thought when 00:15:51.769 --> 00:15:54.090 the data is clean like that. Okay, and finally, 00:15:54.289 --> 00:15:57.129 number nine, strategic reduction. How do you 00:15:57.129 --> 00:15:59.110 decide what information to cut when the context 00:15:59.110 --> 00:16:01.230 gets too big without accidentally losing something 00:16:01.230 --> 00:16:03.600 critical? Right, that's context trimming. And 00:16:03.600 --> 00:16:06.100 the key is it's not about just randomly chopping 00:16:06.100 --> 00:16:08.799 off the end or the beginning. It has to be smart 00:16:08.799 --> 00:16:11.740 trimming. One really effective technique here 00:16:11.740 --> 00:16:15.080 is to use a cheaper, faster, maybe less powerful 00:16:15.080 --> 00:16:18.080 auxiliary AI model to do a kind of relevance 00:16:18.080 --> 00:16:21.059 pre -pass. So imagine you have a massive 50 -page 00:16:21.059 --> 00:16:23.620 document. Instead of feeding that whole monster 00:16:23.620 --> 00:16:26.340 to your powerful, expensive main model, like 00:16:26.340 --> 00:16:28.820 GPT -4, you first pass it through a smaller, 00:16:29.019 --> 00:16:32.330 faster model, maybe like GPT -3 .5. You prompt 00:16:32.330 --> 00:16:34.289 the smaller model, hey, look through this document 00:16:34.289 --> 00:16:36.129 and pull out the five paragraphs that are most 00:16:36.129 --> 00:16:38.629 relevant to Q4 marketing strategy. Then you take 00:16:38.629 --> 00:16:40.970 only those five highly relevant paragraphs and 00:16:40.970 --> 00:16:43.549 feed just those to the big GPT -4 model for the 00:16:43.549 --> 00:16:46.649 actual analysis or generation task. This technique 00:16:46.649 --> 00:16:49.230 can significantly save on API costs while maintaining 00:16:49.230 --> 00:16:51.450 really high quality for the final output. I have 00:16:51.450 --> 00:16:53.509 to admit, I still wrestle with prompt drift myself 00:16:53.509 --> 00:16:55.809 sometimes, so these trimming techniques are absolutely 00:16:55.809 --> 00:16:58.029 vital for keeping my own focus and my costs in 00:16:58.029 --> 00:17:00.889 check. That's a really clever way to leverage 00:17:00.889 --> 00:17:02.610 the different strengths and costs of various 00:17:02.610 --> 00:17:05.650 models. OK, looking across all nine strategies, 00:17:06.009 --> 00:17:08.470 which ones, in your experience, feel like they 00:17:08.470 --> 00:17:11.029 offer the most immediate power or impact for 00:17:11.029 --> 00:17:14.410 practical AI development right now? For immediate 00:17:14.410 --> 00:17:16.569 impact, especially on efficiency and day -to 00:17:16.569 --> 00:17:18.950 -day accuracy. I'd probably say context formatting, 00:17:19.190 --> 00:17:20.950 getting that structured data right, and smart 00:17:20.950 --> 00:17:23.609 trimming. Those feel like game changers you can 00:17:23.609 --> 00:17:26.089 implement pretty quickly. So let's try to bring 00:17:26.089 --> 00:17:28.460 this all together. What does this really mean 00:17:28.460 --> 00:17:31.859 for us, for anyone building or using AI? What's 00:17:31.859 --> 00:17:34.140 the big idea, the main takeaway from diving into 00:17:34.140 --> 00:17:36.920 context engineering? Yeah, the big picture is 00:17:36.920 --> 00:17:39.039 that context engineering represents a really 00:17:39.039 --> 00:17:41.599 fundamental shift in how we approach AI. It's 00:17:41.599 --> 00:17:43.720 the transition from being just an AI user, someone 00:17:43.720 --> 00:17:46.220 who mainly focuses on writing good prompts, to 00:17:46.220 --> 00:17:48.700 becoming more of an AI architect. You're not 00:17:48.700 --> 00:17:50.640 just giving instructions anymore, you're actively 00:17:50.640 --> 00:17:52.980 designing the entire informational environment, 00:17:53.140 --> 00:17:55.759 the flow, the memory, the whole nervous system 00:17:55.759 --> 00:17:58.809 of the AI system itself. And these nine strategies 00:17:58.809 --> 00:18:00.589 we walk through, they aren't really standalone 00:18:00.589 --> 00:18:02.589 formulas you just plug in, are they? They feel 00:18:02.589 --> 00:18:04.910 more like building blocks. Exactly. They're building 00:18:04.910 --> 00:18:07.670 blocks. They're designed to be combined, customized, 00:18:08.029 --> 00:18:10.109 adapted to the specific problem you're trying 00:18:10.109 --> 00:18:13.349 to solve. That's how you build truly sophisticated, 00:18:13.650 --> 00:18:17.089 robust, and reliable AI solutions. It's about 00:18:17.089 --> 00:18:19.950 tackling those immediate, very tangible problems 00:18:19.950 --> 00:18:23.269 we discussed, like high costs and accuracy issues 00:18:23.269 --> 00:18:26.609 that plague so many AI applications today. And 00:18:26.609 --> 00:18:28.630 looking further out, it feels like it's also 00:18:28.630 --> 00:18:30.829 about building the necessary foundation for whatever 00:18:30.829 --> 00:18:34.410 comes next in AI. Yes. Absolutely. It's laying 00:18:34.410 --> 00:18:36.950 the groundwork for the next generation of AI 00:18:36.950 --> 00:18:39.589 applications. Those that might be capable of 00:18:39.589 --> 00:18:41.849 genuine autonomy, continuous learning from their 00:18:41.849 --> 00:18:44.269 environment, and much more intelligent, persistent 00:18:44.269 --> 00:18:46.589 interaction with the world. At the end of the 00:18:46.589 --> 00:18:49.470 day, the future of truly powerful AI probably 00:18:49.470 --> 00:18:52.150 isn't hidden in finding one single perfect prompt, 00:18:52.650 --> 00:18:55.289 but rather in constructing a perfectly architected 00:18:55.289 --> 00:18:57.950 context system. This has really been a deep dive. 00:18:58.029 --> 00:19:00.569 I feel like I have a much clearer map now. I'm 00:19:00.569 --> 00:19:02.809 already thinking about how to apply some of these 00:19:02.809 --> 00:19:04.950 strategies. Thanks so much for walking us through 00:19:04.950 --> 00:19:07.930 this crucial shift. My pleasure. It's definitely 00:19:07.930 --> 00:19:10.849 a fascinating area. And hey, here's maybe a final 00:19:10.849 --> 00:19:12.869 thought for you for the listeners to chew on. 00:19:13.160 --> 00:19:15.559 Think about how designing an AI's memory could 00:19:15.559 --> 00:19:19.180 evolve beyond just text. What if it started incorporating 00:19:19.180 --> 00:19:22.319 sensory data, like inputs from cameras, microphones, 00:19:22.519 --> 00:19:24.940 maybe even touch sensors? What kind of new challenges? 00:19:25.140 --> 00:19:27.759 But also, what incredible new possibilities might 00:19:27.759 --> 00:19:31.140 that open up for future AI agents? Yeah. Sensory 00:19:31.140 --> 00:19:33.180 memory for AI, definitely something to chew on. 00:19:33.539 --> 00:19:35.359 Thanks again. And thanks to all of you for joining 00:19:35.359 --> 00:19:37.099 us on this deep dive. We'll catch you on the 00:19:37.099 --> 00:19:38.779 next one. OETRO music.