WEBVTT 00:00:00.000 --> 00:00:04.179 We've all seen the dazzling potential of retrieval 00:00:04.179 --> 00:00:07.740 augmented generation, RG. You ask your AI a complex 00:00:07.740 --> 00:00:11.279 internal question and it pulls this perfect nuanced 00:00:11.279 --> 00:00:14.359 answer from, you know, your huge stack of internal 00:00:14.359 --> 00:00:17.600 documents. That's the magic. Absolutely. But 00:00:17.600 --> 00:00:19.440 you've probably also seen the horror story, right? 00:00:19.600 --> 00:00:23.480 An AI confidently giving out, say, a customer 00:00:23.480 --> 00:00:25.579 service answer or a financial quote, but it's 00:00:25.579 --> 00:00:27.600 based on a pricing sheet from like two quarters 00:00:27.600 --> 00:00:30.059 ago. Yeah. That's not just wrong. That's actually 00:00:30.059 --> 00:00:32.659 dangerous. That's what we mean by confidently 00:00:32.659 --> 00:00:35.179 unreliable AI. And that really gets to the core 00:00:35.179 --> 00:00:37.899 problem, doesn't it? An AI agent is really only 00:00:37.899 --> 00:00:40.399 as smart, only as reliable as the data you feed 00:00:40.399 --> 00:00:43.780 it. But data isn't static. It's alive. It changes 00:00:43.780 --> 00:00:47.460 constantly. Every day. So today we're diving 00:00:47.460 --> 00:00:49.560 deep into how you build these essential automated 00:00:49.560 --> 00:00:52.020 data pipelines. They act like the guardians of 00:00:52.020 --> 00:00:54.079 your AI's knowledge base, making sure it's always 00:00:54.079 --> 00:00:56.700 in sync, always accurate and, well, trustworthy. 00:00:57.060 --> 00:00:59.560 Welcome to the Deep Dive, everyone. Yeah, we've 00:00:59.560 --> 00:01:02.240 got a fantastic set of resources today, all focused 00:01:02.240 --> 00:01:04.719 on taking RJ systems from just a cool demo to 00:01:04.719 --> 00:01:06.879 something actually reliable for production. Our 00:01:06.879 --> 00:01:09.099 mission today is pretty straightforward. We are 00:01:09.099 --> 00:01:12.120 tackling that chronic problem of expired data. 00:01:12.420 --> 00:01:14.239 We're going to lay out a blueprint, essentially 00:01:14.239 --> 00:01:17.640 no code, for building a fully automated RJ data 00:01:17.640 --> 00:01:20.280 lifecycle manager. We'll talk about tools like 00:01:20.280 --> 00:01:23.659 N8n for the automation part, Google Drive maybe 00:01:23.659 --> 00:01:26.400 as the source for files, and Supabase as the 00:01:26.400 --> 00:01:29.000 vector database. Yep. We'll start with the basic 00:01:29.000 --> 00:01:31.900 pipeline ideas, the foundations. Then we dive 00:01:31.900 --> 00:01:35.099 into the three really critical automated workflows, 00:01:35.359 --> 00:01:37.939 create, update, and delete. We call it the CUD 00:01:37.939 --> 00:01:40.340 system. They have to work together. Okay. And 00:01:40.340 --> 00:01:42.299 finally, we'll touch on how you scale this whole 00:01:42.299 --> 00:01:44.439 thing up using smart routing. Okay. Let's unpack 00:01:44.439 --> 00:01:46.659 that shift first. A lot of teams, when they start 00:01:46.659 --> 00:01:49.260 with RTA, they just upload a folder of PDFs once 00:01:49.260 --> 00:01:52.159 and think they're done. Why do we actually need 00:01:52.159 --> 00:01:55.760 a complex, continuously running pipeline beyond 00:01:55.760 --> 00:01:58.109 just that first upload? Well, because if you 00:01:58.109 --> 00:02:01.129 just rely on that one static upload, your AI 00:02:01.129 --> 00:02:03.370 is basically frozen in time from that moment 00:02:03.370 --> 00:02:06.250 on. Right. The second the first document changes, 00:02:06.349 --> 00:02:09.490 the whole system starts to decay. And an AI with 00:02:09.490 --> 00:02:12.270 an old database, it isn't just a little bit wrong. 00:02:12.629 --> 00:02:14.889 It's confidently wrong about things that really 00:02:14.889 --> 00:02:18.050 matter. That unreliable confidence, I mean, that's 00:02:18.050 --> 00:02:21.189 just a disaster waiting to happen for any business 00:02:21.189 --> 00:02:24.659 trying to use AI for real work. So you need continuous 00:02:24.659 --> 00:02:27.340 kind of invisible synchronization. So we're essentially 00:02:27.340 --> 00:02:30.199 shifting the focus. It's less about endlessly 00:02:30.199 --> 00:02:33.280 tweaking the language model itself and more about 00:02:33.280 --> 00:02:35.180 the plumbing, the infrastructure that supports 00:02:35.180 --> 00:02:37.699 it. Exactly. Think of it like having a brilliant, 00:02:37.759 --> 00:02:40.620 you know, Michelin star chef that's your LLM, 00:02:40.639 --> 00:02:42.819 but you force them to cook with expired ingredients 00:02:42.819 --> 00:02:45.680 in the kitchen. It's a total mess. Yeah. The 00:02:45.680 --> 00:02:47.699 results, no matter how good the chef is, they're 00:02:47.699 --> 00:02:49.319 going to be disappointing, maybe even harmful. 00:02:49.919 --> 00:02:52.379 The data pipeline, that's the hidden hero. It's 00:02:52.379 --> 00:02:54.460 the professional kitchen management system keeping 00:02:54.460 --> 00:02:56.860 everything fresh and organized. The source material 00:02:56.860 --> 00:03:00.219 we looked at outlines three key stages that every 00:03:00.219 --> 00:03:04.419 solid RRAG data pipeline needs to have. It's 00:03:04.419 --> 00:03:05.819 kind of like that professional kitchen workflow 00:03:05.819 --> 00:03:07.840 you mentioned. Stage one is the raw material. 00:03:08.039 --> 00:03:10.099 That's your input, right? The groceries showing 00:03:10.099 --> 00:03:12.120 up at the loading dock. These are your PDFs, 00:03:12.120 --> 00:03:15.060 your Word docs, maybe raw text from a web page. 00:03:15.159 --> 00:03:18.199 Stage two is the processing line. Think of it 00:03:18.199 --> 00:03:20.449 as the prep station. This is where the really 00:03:20.449 --> 00:03:22.490 crucial transformation happens. You clean the 00:03:22.490 --> 00:03:25.009 data, you add important metadata, you break the 00:03:25.009 --> 00:03:27.430 document down into smaller pieces or chunk it, 00:03:27.490 --> 00:03:29.889 and then you generate the embeddings. Let's quickly 00:03:29.889 --> 00:03:32.530 define that jargon. A vector database stores 00:03:32.530 --> 00:03:35.689 embeddings. These are basically the digital fingerprints 00:03:35.689 --> 00:03:38.830 the AI uses to understand and find the right 00:03:38.830 --> 00:03:41.930 information. Exactly. Those fingerprints, they're 00:03:41.930 --> 00:03:44.349 the result of that processing stage. And stage 00:03:44.349 --> 00:03:47.250 three is the final product. The organized searchable 00:03:47.250 --> 00:03:49.689 storage. For a RAG, that's almost always going 00:03:49.689 --> 00:03:51.830 to be your vector database. And every pipeline 00:03:51.830 --> 00:03:53.930 that actually works, no matter the tool you're 00:03:53.930 --> 00:03:56.250 using, it has to be built around four essential 00:03:56.250 --> 00:03:58.229 components. What are those four? That's right. 00:03:58.289 --> 00:04:01.009 First, you've got triggers. That's like the doorbell 00:04:01.009 --> 00:04:03.490 that kicks off the whole process. A notification 00:04:03.490 --> 00:04:05.810 that a file change or something new arrived. 00:04:06.310 --> 00:04:08.710 Second, the inputs. That's just your source files 00:04:08.710 --> 00:04:11.879 themselves. Third is... processing all those 00:04:11.879 --> 00:04:14.939 steps to transform the data into vectors. And 00:04:14.939 --> 00:04:17.279 finally, storage, which is the vector database 00:04:17.279 --> 00:04:20.660 where it all ends up, like Supabase. So why is 00:04:20.660 --> 00:04:23.079 getting these four components right the key to 00:04:23.079 --> 00:04:25.879 reliable AI? It lets you manage the full data 00:04:25.879 --> 00:04:28.720 lifecycle, ensuring your AI is always trustworthy. 00:04:29.019 --> 00:04:31.779 So to get that truly automated, trustworthy AI, 00:04:31.980 --> 00:04:35.180 you can't just stop at creating data. You absolutely 00:04:35.180 --> 00:04:38.079 need three interconnected pipelines working together. 00:04:38.139 --> 00:04:41.240 That's the full CUD system. Create. update delete 00:04:41.240 --> 00:04:43.560 and the source material shows this using nan 00:04:43.560 --> 00:04:46.920 for the automation and google drive as the file 00:04:46.920 --> 00:04:49.779 source but like you said the ideas apply no matter 00:04:49.779 --> 00:04:51.879 the specific tools right it lets us map out the 00:04:51.879 --> 00:04:53.959 whole data life cycle with these low code or 00:04:53.959 --> 00:04:57.540 no code tools So workflow one, initial upload, 00:04:57.740 --> 00:05:00.019 create. This one's the easiest usually. It gets 00:05:00.019 --> 00:05:02.480 triggered when a new file shows up in your designated 00:05:02.480 --> 00:05:05.139 folder. It downloads the file, adds that critical 00:05:05.139 --> 00:05:07.319 metadata. We like calling them digital dog tags. 00:05:07.540 --> 00:05:10.300 And then just inserts the new vectors into your 00:05:10.300 --> 00:05:14.500 database. Simple, clean creation. Then workflow 00:05:14.500 --> 00:05:17.959 two, document update. This kicks off when a file 00:05:17.959 --> 00:05:20.360 gets modified. The file name is the same, but 00:05:20.360 --> 00:05:22.779 the content inside has changed. And this is the 00:05:22.779 --> 00:05:24.699 pipeline that often trips people up, you mentioned, 00:05:24.779 --> 00:05:26.879 because you can't just overwrite the old data. 00:05:27.040 --> 00:05:29.019 You really can't. You have to think of it as 00:05:29.019 --> 00:05:32.199 a very controlled two -step dance. The workflow 00:05:32.199 --> 00:05:34.959 must first delete all the old, outdated vectors 00:05:34.959 --> 00:05:37.480 linked to that file's unique ID. Before adding 00:05:37.480 --> 00:05:40.300 the new ones. Exactly. Then it processes and 00:05:40.300 --> 00:05:42.680 adds the new vectors from the updated file. If 00:05:42.680 --> 00:05:44.439 you miss that deletion step, you just end up 00:05:44.439 --> 00:05:46.699 with junk in your database, old and new answers 00:05:46.699 --> 00:05:48.779 mixed together, conflicting information. It's 00:05:48.779 --> 00:05:51.180 bad. And workflow three, the document deletion 00:05:51.180 --> 00:05:53.759 part. This one uses a pretty clever workaround, 00:05:53.879 --> 00:05:56.079 doesn't it? Since things like Google Drive don't 00:05:56.079 --> 00:05:57.939 always reliably tell you when a file is truly 00:05:57.939 --> 00:06:00.620 deleted, what's the trick? Yeah, the trick is 00:06:00.620 --> 00:06:02.339 you don't wait for a deleted signal that might 00:06:02.339 --> 00:06:05.019 never come. Instead, you treat deletion as an 00:06:05.019 --> 00:06:07.579 action. You manually move the file you want to 00:06:07.579 --> 00:06:10.740 delete into a specific separate folder like a 00:06:10.740 --> 00:06:13.579 recycling bin folder you create. Ah, so the move 00:06:13.579 --> 00:06:16.300 is the trigger. Exactly. That movement into the 00:06:16.300 --> 00:06:19.060 special folder acts as the reliable trigger for 00:06:19.060 --> 00:06:21.069 the third workflow. And that workflow's only 00:06:21.069 --> 00:06:24.029 job is to then run the process to delete the 00:06:24.029 --> 00:06:26.209 corresponding vectors from the database. That's 00:06:26.209 --> 00:06:28.250 actually a really elegant way to handle it, using 00:06:28.250 --> 00:06:31.829 a dedicated folder as the deletion signal. So 00:06:31.829 --> 00:06:34.750 looking at those three, create, update, delete, 00:06:34.870 --> 00:06:38.329 which one would you say is the most technically 00:06:38.329 --> 00:06:41.230 tricky or nuanced to get right? The update pipeline 00:06:41.230 --> 00:06:43.329 is the trickiest because it requires precise 00:06:43.329 --> 00:06:46.110 identification and deletion before re -uploading 00:06:46.110 --> 00:06:48.199 the new data. Okay, so let's dig into the guts 00:06:48.199 --> 00:06:51.040 of that update pipeline. Workflow 2 again. Why 00:06:51.040 --> 00:06:53.300 is metadata, you know, data about your data? 00:06:53.399 --> 00:06:55.959 Why is it so absolutely critical for making both 00:06:55.959 --> 00:06:57.720 the update and the delete pipelines actually 00:06:57.720 --> 00:07:00.720 work? Oh, metadata is your absolute superpower 00:07:00.720 --> 00:07:04.300 here. Seriously. For managing this ARIG lifecycle, 00:07:04.620 --> 00:07:07.139 you usually need at least two critical pieces 00:07:07.139 --> 00:07:10.459 in there. The unique file name. or some kind 00:07:10.459 --> 00:07:12.980 of file id and maybe the last modified date right 00:07:12.980 --> 00:07:15.879 the file name is that specific unique identifier 00:07:15.879 --> 00:07:18.759 the digital dog tag like we said that lets you 00:07:18.759 --> 00:07:21.720 find every single chunk every vector that came 00:07:21.720 --> 00:07:23.560 from that one original source file That makes 00:07:23.560 --> 00:07:25.379 total sense, especially when you think that, 00:07:25.420 --> 00:07:28.160 say, one 10 -page document might get broken down 00:07:28.160 --> 00:07:31.199 into 50 or 60 separate vector chunks in the database. 00:07:31.459 --> 00:07:33.920 Exactly right. You need that single shared ID, 00:07:34.019 --> 00:07:36.439 that common thread, to basically tell the database, 00:07:36.660 --> 00:07:39.199 hey, run a query, delete all the vectors where 00:07:39.199 --> 00:07:41.899 the metadata matches this specific file ID. Without 00:07:41.899 --> 00:07:43.860 that. Without that ID, you're lost. You're trying 00:07:43.860 --> 00:07:46.240 to delete individual atoms without knowing which 00:07:46.240 --> 00:07:49.600 molecule they belong to. It's impossible. I still 00:07:49.600 --> 00:07:52.259 wrestle with managing metadata fields properly 00:07:52.259 --> 00:07:54.519 in my own projects, and I've definitely seen 00:07:54.519 --> 00:07:57.480 workflows fail silently just because, say, the 00:07:57.480 --> 00:07:59.540 case sensitivity of the file name in the metadata 00:07:59.540 --> 00:08:01.899 column didn't perfectly match the new file name. 00:08:02.019 --> 00:08:05.000 If that mapping is off by even one character... 00:08:05.560 --> 00:08:08.620 The whole deletion step just fails quietly, and 00:08:08.620 --> 00:08:11.660 your AI keeps the old wrong answers right alongside 00:08:11.660 --> 00:08:15.139 the new ones. It's a terrifyingly common trap 00:08:15.139 --> 00:08:16.800 when you're automating things. It absolutely 00:08:16.800 --> 00:08:20.040 is. And speaking of traps, there's this one critical, 00:08:20.120 --> 00:08:22.899 really simple setting related to the download 00:08:22.899 --> 00:08:25.120 step after deletion that can save you so much 00:08:25.120 --> 00:08:28.500 headache and, frankly, money. Okay. So after 00:08:28.500 --> 00:08:31.399 the deletion step runs, right, it often outputs 00:08:31.399 --> 00:08:34.139 a whole stream of items. Maybe one little signal 00:08:34.139 --> 00:08:36.500 for every single vector just deleted. Right. 00:08:36.580 --> 00:08:39.139 If it deleted 50 chunks, you get 50 signals coming 00:08:39.139 --> 00:08:42.340 out. Exactly. Now, if you forget to enable this 00:08:42.340 --> 00:08:44.799 simple setting in many tools, it's called something 00:08:44.799 --> 00:08:47.360 like execute only once on the next step, the 00:08:47.360 --> 00:08:49.379 file download step. Uh -oh. Oh, that's right. 00:08:49.419 --> 00:08:51.279 That download node will then try to run multiple 00:08:51.279 --> 00:08:53.559 times completely redundantly. So you could end 00:08:53.559 --> 00:08:55.500 up trying to download and then process the same, 00:08:55.500 --> 00:08:59.580 say, 10 megabyte PDF file 50 times just because 00:08:59.580 --> 00:09:01.879 50 deletion signals triggered the next step 50 00:09:01.879 --> 00:09:03.559 times. It's wasting all that processing power 00:09:03.559 --> 00:09:06.480 in API calls. Precisely. It's crazy inefficient. 00:09:07.019 --> 00:09:10.179 So that simple toggle execute only once, it's 00:09:10.179 --> 00:09:11.960 not just a nice to have. It's really important 00:09:11.960 --> 00:09:15.120 for efficiency. It makes sure the file only gets 00:09:15.120 --> 00:09:18.240 downloaded and processed one time, managing that 00:09:18.240 --> 00:09:20.820 flow from the upstream deletion step. Beyond 00:09:20.820 --> 00:09:23.379 just checking the execution logs for success 00:09:23.379 --> 00:09:26.740 codes, what's the most reliable, simple way to 00:09:26.740 --> 00:09:28.759 actually confirm that the update pipeline really 00:09:28.759 --> 00:09:31.860 worked as intended? Testing the AI agent itself. 00:09:32.179 --> 00:09:34.500 Ask it a question that specifically relies on 00:09:34.500 --> 00:09:36.840 the new information. Confirm me it returns the 00:09:36.840 --> 00:09:39.059 updated policy, not the old deleted one. Okay, 00:09:39.059 --> 00:09:41.559 so once you've got that core CUD system humming 00:09:41.559 --> 00:09:43.840 along, you've pretty much nailed reliability. 00:09:44.220 --> 00:09:46.419 But the next thing is growth, right? What happens 00:09:46.419 --> 00:09:48.080 when suddenly you need to handle more than just 00:09:48.080 --> 00:09:51.480 PDFs? Maybe DOCX files, Excel spreadsheets, markdown 00:09:51.480 --> 00:09:53.320 styles start landing in that knowledge folder. 00:09:53.519 --> 00:09:55.559 Yeah, you definitely don't want to build like... 00:09:55.799 --> 00:09:58.340 a dozen completely separate pipelines all watching 00:09:58.340 --> 00:10:00.259 the same input folder that sounds like a maintenance 00:10:00.259 --> 00:10:02.700 nightmare the source material suggests scaling 00:10:02.700 --> 00:10:04.480 using a single entry point and something they 00:10:04.480 --> 00:10:06.879 call a smart router exactly this smart router 00:10:06.879 --> 00:10:09.600 approach uses a conditional logic node often 00:10:09.600 --> 00:10:11.460 it's just called a switch node and whatever automation 00:10:11.460 --> 00:10:13.840 tool you're using right you still have just one 00:10:13.840 --> 00:10:16.019 single trigger watching that one intake folder 00:10:16.019 --> 00:10:20.139 but then the switch node looks at the file it 00:10:20.139 --> 00:10:23.500 inspects the file extension is it dot pdf Is 00:10:23.500 --> 00:10:26.980 it .txt? Is it .dx? And it intelligently routes 00:10:26.980 --> 00:10:30.120 that file down a specific processing branch that's 00:10:30.120 --> 00:10:32.259 built just for that file type. That's a really 00:10:32.259 --> 00:10:34.220 powerful way to structure it, isn't it? Moving 00:10:34.220 --> 00:10:36.620 from these rigid single -purpose pipelines to 00:10:36.620 --> 00:10:40.639 true dynamic routing based on the input. Two 00:10:40.639 --> 00:10:43.850 -sec silence. Whoa. You can really imagine scaling 00:10:43.850 --> 00:10:46.210 that kind of architecture up, can't you? Handling 00:10:46.210 --> 00:10:47.669 all sorts of different formats coming from maybe 00:10:47.669 --> 00:10:50.149 thousands of sources, dozens of different systems, 00:10:50.190 --> 00:10:52.649 but all converging into one central, unified, 00:10:52.789 --> 00:10:56.450 up -to -date knowledge base. That's really powerful 00:10:56.450 --> 00:10:59.129 data management flexibility. It really is. And 00:10:59.129 --> 00:11:01.690 it makes adding support for new things super 00:11:01.690 --> 00:11:03.889 easy. Let's say next month you need to handle 00:11:03.889 --> 00:11:06.330 audio files or something. You just add a new 00:11:06.330 --> 00:11:08.850 branch to that switch. build out the specific 00:11:08.850 --> 00:11:11.289 processor for audio, and the rest of the system 00:11:11.289 --> 00:11:13.570 just keeps working untouched. That's what true 00:11:13.570 --> 00:11:15.629 scalability looks like. We also need to talk 00:11:15.629 --> 00:11:17.730 about common problems, though, because let's 00:11:17.730 --> 00:11:20.409 be honest, troubleshooting is always half the 00:11:20.409 --> 00:11:22.370 battle when you're building these kinds of automated 00:11:22.370 --> 00:11:26.330 systems. The source gives three specific lifesavers 00:11:26.330 --> 00:11:29.090 for debugging our RAG pipelines. Yeah, the first 00:11:29.090 --> 00:11:31.230 one, like we touched on, is that metadata check. 00:11:31.600 --> 00:11:34.299 If your vectors aren't deleting when they should 00:11:34.299 --> 00:11:37.240 be, it is almost always, always an issue with 00:11:37.240 --> 00:11:40.299 exact case sensitivity or just a tiny mismatch 00:11:40.299 --> 00:11:43.100 in that file name metadata field. Right. The 00:11:43.100 --> 00:11:45.419 smallest typo, a difference in capitalization, 00:11:45.519 --> 00:11:47.740 it breaks the database query trying to find the 00:11:47.740 --> 00:11:50.519 match. So check that first very carefully. And 00:11:50.519 --> 00:11:52.500 the second error sounds like a really nasty one, 00:11:52.539 --> 00:11:54.059 potentially hard to figure out if you don't know 00:11:54.059 --> 00:11:56.419 what you're looking for. The embeddings mismatch 00:11:56.419 --> 00:11:59.620 error. Ugh, it's the worst. If you see that error. 00:12:00.009 --> 00:12:02.590 It means the embedding model you used in your 00:12:02.590 --> 00:12:05.450 automation workflow. Let's say you used OpenAI's 00:12:05.450 --> 00:12:08.789 Text Embedding 3 small model there. That model 00:12:08.789 --> 00:12:11.549 must be the exact same model that your vector 00:12:11.549 --> 00:12:14.610 database is configured to use when it does the 00:12:14.610 --> 00:12:16.870 retrieval lookup. If they don't match. If they 00:12:16.870 --> 00:12:20.110 don't match, the AI's digital fingerprints, those 00:12:20.110 --> 00:12:22.269 vectors, they just don't line up mathematically. 00:12:22.879 --> 00:12:25.340 It means the retrieval completely fails, even 00:12:25.340 --> 00:12:27.120 if the data is actually sitting right there in 00:12:27.120 --> 00:12:29.320 the database. It just can't find it. It's like 00:12:29.320 --> 00:12:31.899 trying to use two different rulers, maybe inches 00:12:31.899 --> 00:12:34.860 and centimeters, to measure the same thing. The 00:12:34.860 --> 00:12:37.200 numbers are accurate in their own system, but 00:12:37.200 --> 00:12:38.919 they're useless when you try to compare them 00:12:38.919 --> 00:12:40.799 directly. That's a perfect analogy. Exactly. 00:12:40.960 --> 00:12:43.259 And the final troubleshooting tip is about... 00:12:43.399 --> 00:12:45.679 Prepping the source files. If you're trying to 00:12:45.679 --> 00:12:50.100 ingest, like, live Google Docs or Google Sheets, 00:12:50.100 --> 00:12:52.659 formats that can change constantly, you absolutely 00:12:52.659 --> 00:12:54.840 need an initial file conversion step. You mean 00:12:54.840 --> 00:12:56.840 before you even start processing the content? 00:12:57.220 --> 00:13:00.039 Yes. You have to turn them into stable, static 00:13:00.039 --> 00:13:03.539 formats first. Convert them to PDF or maybe plain 00:13:03.539 --> 00:13:05.899 text before you send them down the pipeline for 00:13:05.899 --> 00:13:08.879 chunking and embedding. Makes sense. Okay, one 00:13:08.879 --> 00:13:11.429 last question on efficiency here. For a business 00:13:11.429 --> 00:13:13.450 that's growing fast, maybe lots of documents 00:13:13.450 --> 00:13:15.889 changing all the time, what's the biggest advantage 00:13:15.889 --> 00:13:19.169 of maybe switching from real -time file triggers 00:13:19.169 --> 00:13:22.870 to using scheduled scans instead, say checking 00:13:22.870 --> 00:13:26.080 the folder once an hour? Scheduled scans significantly 00:13:26.080 --> 00:13:28.700 reduce the total number of workflow executions 00:13:28.700 --> 00:13:31.700 by processing changes in one single hourly or 00:13:31.700 --> 00:13:34.320 batch job rather than triggering 50 individual 00:13:34.320 --> 00:13:37.379 workflows for 50 small changes. So wrapping this 00:13:37.379 --> 00:13:39.440 up, what does this all really mean for you, the 00:13:39.440 --> 00:13:41.320 listener? I think the big takeaway is that these 00:13:41.320 --> 00:13:44.080 data pipelines, they're the critical, often completely 00:13:44.080 --> 00:13:46.059 invisible infrastructure. They're the essential 00:13:46.059 --> 00:13:48.779 dynamic foundation that separates, you know, 00:13:48.799 --> 00:13:51.360 a cool orange demo from an AI agent you can actually 00:13:51.360 --> 00:13:53.740 rely on in a real production business environment. 00:13:54.250 --> 00:13:57.429 Absolutely. A trustworthy, accurate AI agent, 00:13:57.529 --> 00:14:00.470 it isn't some kind of magic black box technology. 00:14:00.830 --> 00:14:03.929 It's the direct result of carefully building 00:14:03.929 --> 00:14:07.169 this automated create, update, and delete foundation 00:14:07.169 --> 00:14:10.539 correctly. If you don't implement that full CUD 00:14:10.539 --> 00:14:12.620 system, you're basically building a knowledge 00:14:12.620 --> 00:14:14.940 base that's just guaranteed to expire and become 00:14:14.940 --> 00:14:17.679 unreliable. We covered that three -stage architectural 00:14:17.679 --> 00:14:20.340 framework, raw material processing, final product. 00:14:20.559 --> 00:14:22.419 We talked about the four essential components 00:14:22.419 --> 00:14:25.559 for any pipeline triggers, inputs, processing, 00:14:25.639 --> 00:14:28.299 storage. And we really dug into why metadata 00:14:28.299 --> 00:14:31.120 is the absolute key to managing the RAG system 00:14:31.120 --> 00:14:33.779 lifecycle, especially for enabling those tricky 00:14:33.779 --> 00:14:36.139 update and delete workflows. Right, so now that 00:14:36.139 --> 00:14:37.539 you understand how to build this synchronization, 00:14:37.519 --> 00:14:40.120 system this management system the next level 00:14:40.120 --> 00:14:42.600 up the next challenge is really making that system 00:14:42.600 --> 00:14:44.860 smarter think about this for a moment we focus 00:14:44.860 --> 00:14:47.899 today mostly on simple operational metadata right 00:14:47.899 --> 00:14:50.220 things like file name and date but what if you 00:14:50.220 --> 00:14:53.019 started adding more semantic or usage based metadata 00:14:53.019 --> 00:14:55.279 what if you added fields like relevant scores 00:14:55.279 --> 00:14:57.659 may be derived from internal user feedback or 00:14:57.659 --> 00:15:00.940 logs or perhaps tracking access frequency interesting 00:15:00.940 --> 00:15:03.740 that could potentially allow your retrieval system 00:15:03.740 --> 00:15:06.399 not just to find information but to actually 00:15:07.379 --> 00:15:10.620 prioritize knowledge that's highly used or recently 00:15:10.620 --> 00:15:12.899 relevant or highly rated. You could move from 00:15:12.899 --> 00:15:15.620 just reliable data management towards, well, 00:15:15.720 --> 00:15:18.460 truly strategic knowledge retrieval. Now that's 00:15:18.460 --> 00:15:20.200 the challenge for your next deep dive. Thanks 00:15:20.200 --> 00:15:21.980 for diving deep with us today. See you next time.