WEBVTT 00:00:00.000 --> 00:00:05.179 For years, getting AI to really read your documents, 00:00:05.320 --> 00:00:08.980 digest complex PDFs, corporate reports, and then 00:00:08.980 --> 00:00:11.439 critically give you a trustworthy answer, one 00:00:11.439 --> 00:00:15.320 with citations. That took some serious expertise. 00:00:15.519 --> 00:00:17.559 Yeah, a lot of expertise. Kind of had to be a 00:00:17.559 --> 00:00:19.460 coding wizard, right? Right. Wrestling with these 00:00:19.460 --> 00:00:22.879 complex pipelines, spending weeks just hoping 00:00:22.879 --> 00:00:24.699 your text chunks were the right size. It was 00:00:24.699 --> 00:00:26.820 guesswork. A lot of prayer involved, yeah. But 00:00:26.820 --> 00:00:29.800 the surprise is, that whole demanding era, it 00:00:29.800 --> 00:00:32.719 seems that it's, well, officially over. We're 00:00:32.719 --> 00:00:35.380 now in a place where you can build a full -powered 00:00:35.380 --> 00:00:39.299 R agent, one that's citation critical. Minutes. 00:00:39.640 --> 00:00:41.679 Literally minutes. It sounds almost unbelievable 00:00:41.679 --> 00:00:43.560 when you put it like that. Think about the test 00:00:43.560 --> 00:00:45.140 drive example in the source material we've looked 00:00:45.140 --> 00:00:47.659 at. Yeah. An AI that can pull specific financial 00:00:47.659 --> 00:00:50.280 figures from multiple dense corporate reports. 00:00:50.520 --> 00:00:52.979 Okay. And it gives you not just the number, but 00:00:52.979 --> 00:00:55.520 the exact quote and the precise page number. 00:00:55.920 --> 00:00:58.740 So you have immediate verifiable proof right 00:00:58.740 --> 00:01:01.710 there. Welcome to the deep dive. That fundamental 00:01:01.710 --> 00:01:04.250 shift you're talking about, the one that takes 00:01:04.250 --> 00:01:06.750 retrieval augmented generation from this massive 00:01:06.750 --> 00:01:10.430 internal, you know, multi -month project down 00:01:10.430 --> 00:01:12.590 to a five -minute setup. That's exactly what 00:01:12.590 --> 00:01:14.469 we're going to unpack today. Sounds good. And 00:01:14.469 --> 00:01:16.329 just quickly, for anyone maybe newer to this, 00:01:16.370 --> 00:01:19.689 ARAG, retrieval augmented generation, it's basically 00:01:19.689 --> 00:01:22.890 just using your own specific documents to ground 00:01:22.890 --> 00:01:27.019 the AI's answer. Keeps it from making stuff up, 00:01:27.120 --> 00:01:29.500 ensures it's relevant. Right. Stops the hallucination 00:01:29.500 --> 00:01:32.560 problem. Exactly. So our mission today is to 00:01:32.560 --> 00:01:35.150 give you the blueprint. First, we'll dig into 00:01:35.150 --> 00:01:38.209 the accuracy, the verifiable accuracy of these 00:01:38.209 --> 00:01:41.810 super fast agents. Okay. Then we'll expose all 00:01:41.810 --> 00:01:43.969 those old headaches, the old way problems that 00:01:43.969 --> 00:01:46.569 have just vanished. Good riddance. And finally, 00:01:46.689 --> 00:01:48.549 the step -by -step for building one yourself, 00:01:48.629 --> 00:01:50.709 plus some pretty stunning results from a head 00:01:50.709 --> 00:01:52.569 -to -head test. Okay, let's get into it. The 00:01:52.569 --> 00:01:54.650 speed is obviously impressive, you know, faster 00:01:54.650 --> 00:01:56.969 than making coffee. But you said it earlier, 00:01:57.189 --> 00:02:00.819 the real key here feels like trust. it is it's 00:02:00.819 --> 00:02:02.560 like having an agent that acts like a really 00:02:02.560 --> 00:02:04.840 meticulous fact checker someone who always shows 00:02:04.840 --> 00:02:07.939 their work no black boxes and what's fascinating 00:02:07.939 --> 00:02:10.780 is how immediately useful this is for high -stakes 00:02:10.780 --> 00:02:13.039 stuff we're not talking about asking like what's 00:02:13.039 --> 00:02:15.520 the capital of france right this is serious data 00:02:15.520 --> 00:02:18.379 we're talking about querying a really complex 00:02:18.379 --> 00:02:20.900 knowledge base specifically corporate financial 00:02:20.900 --> 00:02:24.740 reports dense pdfs okay so give us an example 00:02:24.740 --> 00:02:26.599 creator what would you ask imagine asking something 00:02:26.599 --> 00:02:30.789 like What was Tesla's total revenue for Q2 2025 00:02:30.789 --> 00:02:35.289 based on their report? Or maybe NVIDIA's Q1 fiscal 00:02:35.289 --> 00:02:38.830 year 25 revenue? Specific questions into specific 00:02:38.830 --> 00:02:41.330 documents. And the output. That's the magic part. 00:02:41.430 --> 00:02:43.550 That's the breakthrough, yeah. It delivers the 00:02:43.550 --> 00:02:46.490 accurate number, sure. But crucially, it also 00:02:46.490 --> 00:02:49.990 gives you the exact... document name, the specific 00:02:49.990 --> 00:02:52.490 page number where it found it, and the verbatim 00:02:52.490 --> 00:02:54.889 quote straight out of the original PDF. So you 00:02:54.889 --> 00:02:56.509 could check it instantly. Instantly. We looked 00:02:56.509 --> 00:02:58.289 at the source details for that Tesla revenue 00:02:58.289 --> 00:03:01.250 query. The system cited page four of the Q2 report. 00:03:01.469 --> 00:03:03.789 You open the PDF, go to page four, and boom, 00:03:03.889 --> 00:03:07.490 there's the data point. Flawless. Wow. That level 00:03:07.490 --> 00:03:10.750 of granular traceable proof, that's kind of the 00:03:10.750 --> 00:03:12.789 holy grail if you're making critical decisions 00:03:12.789 --> 00:03:15.169 based on this information. OK, so here's a question 00:03:15.169 --> 00:03:17.990 then. How does having that instant high fidelity 00:03:17.990 --> 00:03:20.789 citation actually change how a professional, 00:03:20.949 --> 00:03:24.199 you know. consumes data or makes decisions day 00:03:24.199 --> 00:03:27.080 to day? Well, it fundamentally shifts AI from 00:03:27.080 --> 00:03:30.860 being a potential guesser to a provider of verifiable 00:03:30.860 --> 00:03:34.340 documented proof. Answers become actionable immediately. 00:03:34.840 --> 00:03:38.659 Okay. So AI goes from maybe to fact. Got it. 00:03:38.699 --> 00:03:40.500 Now, let's talk about what we're leaving behind 00:03:40.500 --> 00:03:43.319 because it's important context. The old way of 00:03:43.319 --> 00:03:45.680 building our gag, honestly, it was an obstacle 00:03:45.680 --> 00:03:48.259 course. Ah, sounds all right. The source material 00:03:48.259 --> 00:03:50.759 compares it to using a stone axe versus a power 00:03:50.759 --> 00:03:52.860 tool. And that feels pretty accurate. It was 00:03:52.860 --> 00:03:55.120 manual. It was fragile. OK, but some people might 00:03:55.120 --> 00:03:56.699 hear this and think, is this just, you know, 00:03:56.699 --> 00:03:59.120 a slick wrapper? What was so bad about the old 00:03:59.120 --> 00:04:01.240 way? Why call this revolutionary? Oh, the pain 00:04:01.240 --> 00:04:03.659 points were real and they cost weeks of developer 00:04:03.659 --> 00:04:06.439 time easily. First off, you had the Goldilocks 00:04:06.439 --> 00:04:08.860 problem with tech splitting. Ah, yes. Chunking. 00:04:09.310 --> 00:04:12.050 Exactly. You had to manually figure out the perfect 00:04:12.050 --> 00:04:15.229 chunk size, not too big, or the AI loses the 00:04:15.229 --> 00:04:18.829 thread, not too small, or you split a key fact 00:04:18.829 --> 00:04:21.670 across two different chunks. Nightmare. We used 00:04:21.670 --> 00:04:25.329 to spend ages, sometimes months, just experimenting 00:04:25.329 --> 00:04:28.350 with custom embeddings, trying to manage metadata 00:04:28.350 --> 00:04:31.230 correctly, wrestling with setting up and then 00:04:31.230 --> 00:04:33.930 maintaining a vector database. Oh, the vector 00:04:33.930 --> 00:04:36.129 database maintenance. Right. It often felt like 00:04:36.129 --> 00:04:37.790 running a whole separate piece of infrastructure 00:04:37.790 --> 00:04:40.310 just for the look. Yeah, I remember this one 00:04:40.310 --> 00:04:43.709 project maybe two years back. We spent weeks 00:04:43.709 --> 00:04:45.970 just trying to get the text splitting right for 00:04:45.970 --> 00:04:48.370 these legal docs. Oh, I bet. Nightmare. And then 00:04:48.370 --> 00:04:50.410 we realized the metadata for the clauses wasn't 00:04:50.410 --> 00:04:53.860 even indexed properly. The AI just couldn't tell 00:04:53.860 --> 00:04:57.040 an NDA from a settlement. Honestly, I still wrestle 00:04:57.040 --> 00:04:59.040 with prompt drift sometimes myself. It really 00:04:59.040 --> 00:05:01.160 felt like a full -time job just managing that 00:05:01.160 --> 00:05:03.579 backend stuff. Exactly. Instead of focusing on 00:05:03.579 --> 00:05:05.920 the actual intelligence and that kind of complexity, 00:05:06.040 --> 00:05:08.800 it's just absorbed now, handled by the platform. 00:05:09.180 --> 00:05:12.720 And here's where it gets really cool. The backend 00:05:12.720 --> 00:05:15.500 magic that handles all that messy work automatically. 00:05:15.959 --> 00:05:18.939 The source calls it an AI dream team working 00:05:18.939 --> 00:05:21.949 behind the scenes. So this dream team. It has 00:05:21.949 --> 00:05:24.120 different roles. Like the smart librarian, the 00:05:24.120 --> 00:05:26.560 master butcher. What's the master butcher doing 00:05:26.560 --> 00:05:28.420 that's so much better than just hitting split 00:05:28.420 --> 00:05:30.319 document? That seems like the biggest leap. A 00:05:30.319 --> 00:05:32.720 master butcher isn't just like cutting text every 00:05:32.720 --> 00:05:35.480 500 characters. It's using advanced algorithms. 00:05:35.480 --> 00:05:38.420 It actually analyzes the structure of the document, 00:05:38.600 --> 00:05:41.480 the headers, paragraphs, tables. Oh, okay. Context 00:05:41.480 --> 00:05:44.439 aware. Precisely. It chunks intelligently, respecting 00:05:44.439 --> 00:05:47.279 the semantic boundaries. That alone is a massive 00:05:47.279 --> 00:05:50.240 step up from manual or simple splitting. Gotcha. 00:05:50.379 --> 00:05:52.300 Then you've got the smart librarian. handles 00:05:52.300 --> 00:05:55.339 ingesting all sorts of files, PDFs, Word docs, 00:05:55.540 --> 00:05:58.519 and understands their structure. And Fact Checker 00:05:58.519 --> 00:06:01.180 makes sure every single piece of info is tracked 00:06:01.180 --> 00:06:03.939 back to its source for those citations. Right, 00:06:04.019 --> 00:06:06.860 the citations again. So if the backend handles 00:06:06.860 --> 00:06:08.500 the splitting and the indexing automatically, 00:06:09.000 --> 00:06:11.519 what's the biggest cost saving there beyond just 00:06:11.519 --> 00:06:14.060 saving developer setup time, which is already 00:06:14.060 --> 00:06:16.759 huge, obviously? The biggest win, honestly, is 00:06:16.759 --> 00:06:19.480 probably avoiding the ongoing cost and inefficiency 00:06:19.480 --> 00:06:22.920 of manual maintenance. And critically important, 00:06:23.160 --> 00:06:26.040 cutting down wasteful token consumption during 00:06:26.040 --> 00:06:28.759 queries. Explain that token part. Well, traditional 00:06:28.759 --> 00:06:32.160 systems often have to send way, way more context, 00:06:32.240 --> 00:06:35.040 like 20 times the necessary text to the AI just 00:06:35.040 --> 00:06:37.180 to find one simple fact, because the chunking 00:06:37.180 --> 00:06:39.480 wasn't precise. That burns through tokens and 00:06:39.480 --> 00:06:41.680 tokens cost money. Okay. Massive efficiency, 00:06:41.939 --> 00:06:45.589 Jane, then. Avoiding waste. Huge. We've covered 00:06:45.589 --> 00:06:48.709 the why, the pain relief, the cost savings. Let's 00:06:48.709 --> 00:06:50.829 get practical. Let's talk about the how. The 00:06:50.829 --> 00:06:52.829 blueprint for actually building one of these. 00:06:52.870 --> 00:06:55.029 It seems surprisingly clear, right? Three phases. 00:06:55.290 --> 00:06:57.509 Yeah, seems very logical. Phase one is setting 00:06:57.509 --> 00:06:59.509 up the brain, the assistant itself. Right. You 00:06:59.509 --> 00:07:01.149 basically just create the assistant, give it 00:07:01.149 --> 00:07:03.389 a clear job title like financial report analyst 00:07:03.389 --> 00:07:05.250 or something descriptive. Makes sense. And then 00:07:05.250 --> 00:07:07.029 you feed it knowledge. And this is the kicker. 00:07:07.470 --> 00:07:10.089 You just drag and drop your files, your complex 00:07:10.089 --> 00:07:12.970 PDFs, Word docs, whatever. The system handles 00:07:12.970 --> 00:07:16.069 all the hard parts, instantly chunking, indexing, 00:07:16.069 --> 00:07:19.470 vectorizing. Oh, okay. No manual preprocessing? 00:07:19.509 --> 00:07:22.069 None. Then you can test it right away in the 00:07:22.069 --> 00:07:24.009 built -in chat play button. See how it responds. 00:07:24.350 --> 00:07:27.319 Okay. Brain setup sounds fast. then phase two 00:07:27.319 --> 00:07:29.879 is the hands connecting it to your workflow. 00:07:30.060 --> 00:07:32.519 Exactly. Getting that knowledge base talking 00:07:32.519 --> 00:07:35.699 to other tools you might use, like NAN or Zapier 00:07:35.699 --> 00:07:38.759 maybe. You use the chat API so external apps 00:07:38.759 --> 00:07:41.639 can ping your new knowledge brain. And the source 00:07:41.639 --> 00:07:43.300 mentioned something clever for setup there, the 00:07:43.300 --> 00:07:46.180 curl import feature. Yeah. Makes connecting easier. 00:07:46.500 --> 00:07:48.439 Yeah, it's a neat shortcut. It basically pre 00:07:48.439 --> 00:07:51.100 -configures the HTTP request node for you so 00:07:51.100 --> 00:07:52.740 you don't have to manually set up headers and 00:07:52.740 --> 00:07:55.560 stuff. But the really key technical bit is that 00:07:55.560 --> 00:07:58.540 dynamic query replacement from AI and search 00:07:58.540 --> 00:08:00.959 query. Okay, unpack that dynamic query thing. 00:08:01.040 --> 00:08:02.759 Why is that so important? Okay, think of it like 00:08:02.759 --> 00:08:06.279 this. Your main AI, maybe your general chatbot, 00:08:06.420 --> 00:08:09.579 is the conductor. The ARAG agent with the documents 00:08:09.579 --> 00:08:12.779 is the special. orchestra section. Got it. That 00:08:12.779 --> 00:08:15.459 dynamic query tells the conductor AI to figure 00:08:15.459 --> 00:08:17.920 out the specific, precise question to ask the 00:08:17.920 --> 00:08:20.220 orchestra section based on the user's broader 00:08:20.220 --> 00:08:22.920 conversation. Ah, so it doesn't just dump the 00:08:22.920 --> 00:08:25.579 whole chat history into the ARAG agent. Exactly. 00:08:25.660 --> 00:08:28.480 It avoids sending tons of irrelevant context, 00:08:28.720 --> 00:08:31.120 which saves a huge amount of tokens and makes 00:08:31.120 --> 00:08:34.259 the search query laser -focused, much more efficient. 00:08:34.440 --> 00:08:38.559 Clever. Okay, phase three, the intelligence boost. 00:08:39.420 --> 00:08:42.340 Fine -tuning. This starts with the rulebook, 00:08:42.480 --> 00:08:45.759 the system prompt. Yes, and we really can't overstate 00:08:45.759 --> 00:08:48.139 how vital the system prompt is. It's the difference 00:08:48.139 --> 00:08:50.200 between just getting an answer and getting a 00:08:50.200 --> 00:08:52.899 trustworthy answer. It's the AI's core instructions. 00:08:53.340 --> 00:08:55.440 It's the rulebook you set before the user even 00:08:55.440 --> 00:08:58.000 asks anything. You tell it its personality, its 00:08:58.000 --> 00:09:00.480 constraints. You insist, for example. Always 00:09:00.480 --> 00:09:03.539 provide full citations, document name, page number, 00:09:03.639 --> 00:09:06.419 section, and an exact quoted excerpt. Make it 00:09:06.419 --> 00:09:08.570 non -negotiable. So what's the real practical 00:09:08.570 --> 00:09:10.710 difference between doing that in the system prompt 00:09:10.710 --> 00:09:13.169 versus just telling the AI what you want in the 00:09:13.169 --> 00:09:15.210 first chat message you send it? The system prompt 00:09:15.210 --> 00:09:17.909 defines the AI's persistent internal rules and 00:09:17.909 --> 00:09:20.129 its specialty. It's baked in. Instructions in 00:09:20.129 --> 00:09:22.049 a chat message are just temporary context for 00:09:22.049 --> 00:09:24.230 that one conversation. The system prompt is its 00:09:24.230 --> 00:09:26.389 core operating instructions before it even looks 00:09:26.389 --> 00:09:28.350 at the user's query. Okay, so it sets the fundamental 00:09:28.350 --> 00:09:31.490 behavior. Got it. And crucially, you mentioned 00:09:31.490 --> 00:09:34.289 this earlier, you must demand those verbatim 00:09:34.289 --> 00:09:36.649 quotes. That's the switch that turns it from 00:09:36.649 --> 00:09:39.529 just a summarizer into a proper fact checker. 00:09:39.549 --> 00:09:41.840 How do you flip that switch technically? Yeah, 00:09:41.960 --> 00:09:44.779 it's a specific parameter in the API call. You 00:09:44.779 --> 00:09:47.519 add include highlights. Okay. That forces the 00:09:47.519 --> 00:09:50.480 agent to pull the exact source text segments 00:09:50.480 --> 00:09:52.720 it used to generate the answer. It gives you 00:09:52.720 --> 00:09:56.220 that undeniable proof. Without it, the AI might 00:09:56.220 --> 00:09:59.039 paraphrase, and paraphrasing can accidentally 00:09:59.039 --> 00:10:01.960 introduce errors or change nuance. Right, especially 00:10:01.960 --> 00:10:04.960 with precise financial or legal text. Absolutely. 00:10:05.019 --> 00:10:07.419 And this fine -tuning stage is also where you 00:10:07.419 --> 00:10:09.779 choose your model, GPT -40, Claude, whatever 00:10:09.779 --> 00:10:11.970 works best, and... Tweak the temperature. Lower 00:10:11.970 --> 00:10:13.669 temperature for facts, right? Exactly. You want 00:10:13.669 --> 00:10:16.250 it low for factual consistency. Keep creativity 00:10:16.250 --> 00:10:18.250 out of financial reporting. Two sec silence. 00:10:18.629 --> 00:10:22.169 That integration, making verifiable proof just 00:10:22.169 --> 00:10:24.909 part of the standard output, that really does 00:10:24.909 --> 00:10:26.870 feel like the game changer here. And that leads 00:10:26.870 --> 00:10:29.509 us perfectly to the showdown, the moment of truth. 00:10:30.090 --> 00:10:32.730 The sources described a direct comparison test, 00:10:33.009 --> 00:10:36.269 high stakes query. What was Tesla's operating 00:10:36.269 --> 00:10:39.990 margin in Q2 2025? The known documented answer 00:10:39.990 --> 00:10:44.250 was 4 .1%. Okay, so a clear target. How did they 00:10:44.250 --> 00:10:47.669 do? It was frankly a knockout for this new simplified 00:10:47.669 --> 00:10:51.429 approach. The assistant nailed it. Perfect 4 00:10:51.429 --> 00:10:56.090 .1 % flawless citation using only about 12 ,260 00:10:56.090 --> 00:10:58.649 tokens. Wow, that's lean. And the traditional 00:10:58.649 --> 00:11:01.210 RAG -G setup. The one that took weeks to build 00:11:01.210 --> 00:11:03.710 and tune. It was often just plain wrong in its 00:11:03.710 --> 00:11:05.710 answer, and it shooed through around 30 ,000 00:11:05.710 --> 00:11:08.029 tokens to get there. 30 ,000. Compared to 1200. 00:11:08.289 --> 00:11:11.389 Yep. 23 times more expensive on tokens, slower, 00:11:11.690 --> 00:11:13.809 less reliable, and the source attribution was 00:11:13.809 --> 00:11:16.129 weak. No contest, really. The bottom line there 00:11:16.129 --> 00:11:18.450 seems crystal clear. You're saving, what, 20, 00:11:18.549 --> 00:11:21.029 40 hours of dev setup time per project? Easily, 00:11:21.129 --> 00:11:23.649 sometimes more. And slashing those ongoing operational 00:11:23.649 --> 00:11:26.190 costs, the token bills, it completely changes 00:11:26.190 --> 00:11:27.750 the economics, doesn't it? Makes this kind of 00:11:27.750 --> 00:11:30.149 power accessible to way more teams. Absolutely. 00:11:30.570 --> 00:11:33.870 Which leads to thinking about scaling. This simplicity 00:11:33.870 --> 00:11:38.549 makes it feasible to think bigger, right? Creating 00:11:38.549 --> 00:11:40.830 specialized AI libraries. But let's say domain 00:11:40.830 --> 00:11:43.370 specialization, yeah. So instead of one giant 00:11:43.370 --> 00:11:45.950 know -it -all AI, you build experts. Exactly. 00:11:46.009 --> 00:11:48.429 Think about it. You could spin up a legal document 00:11:48.429 --> 00:11:51.230 analyst totally focused on compliance language 00:11:51.230 --> 00:11:54.129 and contracts. Okay. Then a separate financial 00:11:54.129 --> 00:11:56.710 report processor, only fed earnings calls and 00:11:56.710 --> 00:11:59.909 SEC filings. Maybe a research paper analyst for 00:11:59.909 --> 00:12:02.450 scientific literature. They stay hyper -focused, 00:12:02.750 --> 00:12:05.350 no knowledge contamination. That makes a lot 00:12:05.350 --> 00:12:07.600 of sense. Of course, scaling like that still 00:12:07.600 --> 00:12:10.080 needs some governance, right? Access control, 00:12:10.240 --> 00:12:12.460 monitoring. Definitely. You absolutely need things 00:12:12.460 --> 00:12:15.440 like user access control. Got to keep sensitive 00:12:15.440 --> 00:12:17.700 HR docs separate from public marketing materials, 00:12:17.879 --> 00:12:20.039 for example. Right. And performance monitoring 00:12:20.039 --> 00:12:23.279 is key. Tracking accuracy, response times, and 00:12:23.279 --> 00:12:25.620 especially that token usage to make sure you're 00:12:25.620 --> 00:12:27.799 maintaining that incredible efficiency. You have 00:12:27.799 --> 00:12:30.960 to keep an eye on it. But just, whoa. Imagine 00:12:30.960 --> 00:12:33.639 scaling this kind of capability with that level 00:12:33.639 --> 00:12:36.820 of token efficiency. Think about handling terabytes 00:12:36.820 --> 00:12:40.259 of internal documentation or analyzing, I don't 00:12:40.259 --> 00:12:42.539 know, a billion customer support queries a year. 00:12:42.600 --> 00:12:44.820 It just completely changes the financial viability. 00:12:45.059 --> 00:12:47.679 Yeah, shifts AI from purely a cost center experiment 00:12:47.679 --> 00:12:51.580 to a massive efficiency engine. So given how 00:12:51.580 --> 00:12:55.059 efficient and easy this new way seems. When would 00:12:55.059 --> 00:12:58.480 a big company still choose the old super complex 00:12:58.480 --> 00:13:01.200 custom RA build? Is there still a place for it? 00:13:01.220 --> 00:13:03.440 Honestly, it's becoming a very niche requirement, 00:13:03.639 --> 00:13:06.259 really only for the most extreme massive scale 00:13:06.259 --> 00:13:08.259 deployments. We're talking petabytes maybe. Or 00:13:08.259 --> 00:13:10.120 if you have some incredibly unique specialized 00:13:10.120 --> 00:13:13.059 data that requires a very specific custom trained 00:13:13.059 --> 00:13:16.000 embedding model, maybe for like analyzing obscure 00:13:16.000 --> 00:13:18.679 ancient texts or highly specialized medical imaging 00:13:18.679 --> 00:13:20.879 data where off the shelf models just won't cut 00:13:20.879 --> 00:13:23.899 it. So for 99 % of typical business use cases. 00:13:24.019 --> 00:13:26.480 For 99 % of business cases, this simplified, 00:13:26.659 --> 00:13:28.139 efficient approach is going to be the winner. 00:13:28.320 --> 00:13:30.740 Hands down. Okay, so let's try and synthesize 00:13:30.740 --> 00:13:34.379 this. The big idea, the core concept here, is 00:13:34.379 --> 00:13:37.279 that the AI dream of having instantly searchable, 00:13:37.320 --> 00:13:40.000 trustworthy knowledge from your own documents. 00:13:41.000 --> 00:13:44.039 It's finally here. And it arrived not through 00:13:44.039 --> 00:13:46.259 more complexity, but through radical simplification. 00:13:46.500 --> 00:13:49.840 Exactly. By focusing on citation quality, cost 00:13:49.840 --> 00:13:52.259 efficiency, and just sheer speed of deployment. 00:13:52.559 --> 00:13:56.200 The revolution isn't just better AI. It's democratizing 00:13:56.200 --> 00:13:59.220 access to it by removing those huge hurdles like 00:13:59.220 --> 00:14:01.779 manual chunking and database management. Couldn't 00:14:01.779 --> 00:14:03.799 say it better. And just a quick reminder on best 00:14:03.799 --> 00:14:05.779 practices if you do decide to build one of these. 00:14:05.919 --> 00:14:08.860 Garbage in, garbage out still applies. Use high 00:14:08.860 --> 00:14:11.139 quality source documents. Meaning searchable 00:14:11.139 --> 00:14:14.659 PDFs. Good OCR. Yep. Make sure the text is clean, 00:14:14.820 --> 00:14:17.240 ask specific questions, and keep an eye on those 00:14:17.240 --> 00:14:19.220 system prompts, refine them over time as you 00:14:19.220 --> 00:14:21.279 see how the AI behaves. Continuous improvement. 00:14:21.679 --> 00:14:24.240 Always. And looking ahead, this shift points 00:14:24.240 --> 00:14:26.159 towards some exciting future trends, doesn't 00:14:26.159 --> 00:14:28.779 it? We should probably expect even smarter integrations 00:14:28.779 --> 00:14:30.980 into the tools people already use. Yeah, less 00:14:30.980 --> 00:14:33.960 context switching. Multimodal mastery seems inevitable, 00:14:34.299 --> 00:14:38.840 querying not just text, but images, audio, maybe 00:14:38.840 --> 00:14:41.460 video snippets, all linked back to the source. 00:14:42.210 --> 00:14:44.610 Asking questions about a chart in the PDF. Exactly. 00:14:45.009 --> 00:14:47.610 And deeper reasoning capabilities build on top 00:14:47.610 --> 00:14:50.830 of this verifiable knowledge foundation. So the 00:14:50.830 --> 00:14:54.029 takeaway isn't really if this simplified, verifiable 00:14:54.029 --> 00:14:56.750 RA approach becomes the standard. It feels like 00:14:56.750 --> 00:14:58.830 it already is or soon will be. Yeah, the question 00:14:58.830 --> 00:15:01.149 really is how quickly will you adopt these tools? 00:15:01.309 --> 00:15:03.350 They're incredibly powerful, surprisingly easy 00:15:03.350 --> 00:15:06.149 to set up, and they can genuinely transform how 00:15:06.149 --> 00:15:08.269 you access information and make decisions. The 00:15:08.269 --> 00:15:10.450 power is there for the taking now. Stop waiting 00:15:10.450 --> 00:15:12.809 for some coding. wizard to build your AI knowledge 00:15:12.809 --> 00:15:14.990 base for you. Go become the wizard yourself.