WEBVTT 00:00:00.000 --> 00:00:01.620 You know, it's a common story. You build an AI 00:00:01.620 --> 00:00:03.819 application, maybe a retrieval system, something 00:00:03.819 --> 00:00:06.900 based on your own data. Yeah. And, well, it just 00:00:06.900 --> 00:00:09.400 doesn't quite work reliably, does it? Oh, yeah. 00:00:09.599 --> 00:00:13.080 That initial excitement hits reality fast. Right. 00:00:13.240 --> 00:00:16.120 It makes things up, hallucinates, as they say, 00:00:16.179 --> 00:00:19.160 or it just completely misses information. Yeah. 00:00:19.379 --> 00:00:22.179 You know, it feels brittle. That's the classic 00:00:22.179 --> 00:00:25.140 failure point. And honestly, the big lesson everyone 00:00:25.140 --> 00:00:28.329 learns moving past a simple demo is that Basic 00:00:28.329 --> 00:00:31.230 ARAG Retrieval Augmented Generation. Well, it's 00:00:31.230 --> 00:00:33.409 kind of stupid. It's just too simple for the 00:00:33.409 --> 00:00:36.049 real world. Exactly. So, welcome to the Deep 00:00:36.049 --> 00:00:38.310 Dive. Today, we're going past that frustration. 00:00:38.549 --> 00:00:40.950 We're digging into some really solid source material 00:00:40.950 --> 00:00:45.490 that lays out 11 advanced RVIG strategies. Strategies 00:00:45.490 --> 00:00:48.509 designed to fix those exact real -world problems. 00:00:48.750 --> 00:00:50.670 Our goal here is to give you the roadmap, the 00:00:50.670 --> 00:00:52.729 sort of strategic stack you need to turn that 00:00:52.729 --> 00:00:54.869 brittle demo into something genuinely production 00:00:54.869 --> 00:00:57.229 -ready. Something that actually delivers reliable 00:00:57.229 --> 00:00:59.810 value. Okay, so maybe let's quickly set the stage. 00:01:00.079 --> 00:01:02.020 What is this naive areg we're talking about? 00:01:02.240 --> 00:01:04.319 Good point. It's really a simple pattern, four 00:01:04.319 --> 00:01:07.040 steps. First, you chunk your documents. You just 00:01:07.040 --> 00:01:09.560 chop them up into pieces. Usually based on some 00:01:09.560 --> 00:01:12.180 arbitrary token count. Right. Then you embed 00:01:12.180 --> 00:01:15.620 those chunks, turn the text into vectors, numbers 00:01:15.620 --> 00:01:18.239 basically, so a computer can search for meaning, 00:01:18.280 --> 00:01:20.620 not just keywords. Like giving each piece of 00:01:20.620 --> 00:01:23.420 text an address on a big map of concepts. Exactly. 00:01:23.780 --> 00:01:28.079 Then, step three, retrieve. You search that map 00:01:28.079 --> 00:01:30.379 and grab the top few chunks, maybe three to five, 00:01:30.599 --> 00:01:32.959 that seem closest to the user's query vector. 00:01:33.159 --> 00:01:36.180 And finally, generate. You feed those retrieved 00:01:36.180 --> 00:01:38.859 chunks, plus the original question, to a large 00:01:38.859 --> 00:01:41.920 language model, an LLM, and ask it to synthesize 00:01:41.920 --> 00:01:44.359 an answer. Simple pipeline. Simple, but like 00:01:44.359 --> 00:01:47.140 we said, deeply flawed when things get complex. 00:01:47.480 --> 00:01:50.140 Okay, let's unpack why. Why does that simple 00:01:50.140 --> 00:01:52.400 process just fall apart so quickly in the real 00:01:52.400 --> 00:01:55.230 world? Especially with messy data like internal 00:01:55.230 --> 00:01:58.010 reports or legal docs. Yeah, it boils down to 00:01:58.010 --> 00:02:00.750 about four major failure points that really tank 00:02:00.750 --> 00:02:02.730 the quality. First off, the retrieval quality 00:02:02.730 --> 00:02:05.489 itself often just isn't good enough. That's semantic 00:02:05.489 --> 00:02:08.810 search, the vector search. It's fast, yeah, but 00:02:08.810 --> 00:02:10.870 mathematically pretty simple. It's just looking 00:02:10.870 --> 00:02:13.750 for closeness in that vector space. So it can 00:02:13.750 --> 00:02:16.729 easily miss the actual best answer if that answer 00:02:16.729 --> 00:02:19.129 happens to rank, say, seventh or eighth, and 00:02:19.129 --> 00:02:21.050 you only ask for the top five. The system doesn't 00:02:21.050 --> 00:02:23.610 even see it. Wow. So the perfect answer might 00:02:23.610 --> 00:02:26.009 be sitting right there, just outside the window 00:02:26.009 --> 00:02:28.580 you chose. That feels incredibly inefficient. 00:02:28.939 --> 00:02:31.939 It is. And second, there's the huge problem of 00:02:31.939 --> 00:02:34.639 context fragmentation, that arbitrary chunking 00:02:34.639 --> 00:02:37.379 we mentioned. It's like putting a crucial document 00:02:37.379 --> 00:02:40.340 through a paper shredder. You destroy the connections, 00:02:40.439 --> 00:02:43.139 the relationships between sentences and paragraphs. 00:02:43.500 --> 00:02:45.900 What's the biggest threat from that context fragmentation? 00:02:45.960 --> 00:02:48.319 Like, what's the real damage? You lose the full 00:02:48.319 --> 00:02:51.139 meaning because vital connecting sentences get 00:02:51.139 --> 00:02:53.659 separated. Can you give an example? Sure. Imagine 00:02:53.659 --> 00:02:57.500 a contract chunk says, The penalty is 10%. Sounds 00:02:57.500 --> 00:03:00.340 clear, right? But the sentence right before it, 00:03:00.419 --> 00:03:03.080 which defined whether that 10 % applied to gross 00:03:03.080 --> 00:03:05.699 revenue or net profit, that got chopped into 00:03:05.699 --> 00:03:08.199 a different chunk. So the retrieved answer, the 00:03:08.199 --> 00:03:11.680 penalty is 10%, is now totally useless or maybe 00:03:11.680 --> 00:03:14.000 even dangerously misleading because the defining 00:03:14.000 --> 00:03:17.400 context is gone, shredded. Yikes. Okay, what 00:03:17.400 --> 00:03:21.319 else? Third. Queries are ambiguous. Users don't 00:03:21.319 --> 00:03:23.139 always ask perfect questions. They ask things 00:03:23.139 --> 00:03:26.639 like, tell me about Q3 performance. Right. Vague. 00:03:26.800 --> 00:03:29.599 And the basic RS system has no idea what that 00:03:29.599 --> 00:03:31.439 really means. Should it check the financial database, 00:03:31.759 --> 00:03:34.400 sales reports, customer support tickets? It just 00:03:34.400 --> 00:03:37.360 kind of guesses or defaults to one source, often 00:03:37.360 --> 00:03:39.240 missing the bigger picture. And the fourth failure 00:03:39.240 --> 00:03:42.060 point. Finally, responses lack verification. 00:03:42.639 --> 00:03:45.879 The LLM generates its answer, answer V1, and 00:03:45.879 --> 00:03:48.319 that's it. There's no built -in step for it to 00:03:48.319 --> 00:03:50.699 pause, double -check its work against the retrieved 00:03:50.699 --> 00:03:52.979 sources, or verify if it's even fully answered 00:03:52.979 --> 00:03:54.860 the question. It just spits out the first thing 00:03:54.860 --> 00:03:57.219 it comes up with. So just to recap that retrieval 00:03:57.219 --> 00:03:59.599 point, why does that simple retrieval process 00:03:59.599 --> 00:04:03.020 often fail? Because the initial search just matches 00:04:03.020 --> 00:04:05.539 words or concepts. It doesn't confirm semantic 00:04:05.539 --> 00:04:09.020 completeness or context. Okay, that paints a 00:04:09.020 --> 00:04:11.259 pretty clear picture of the problem. The good 00:04:11.259 --> 00:04:13.360 news, as you mentioned, is we have strategies. 00:04:13.620 --> 00:04:15.860 We don't need all 11 at once, right? We need 00:04:15.860 --> 00:04:17.399 the ones that give the most bang for the buck 00:04:17.399 --> 00:04:19.759 first. Let's talk about that baseline stack, 00:04:19.839 --> 00:04:21.800 the things you really should implement. Absolutely. 00:04:21.939 --> 00:04:24.399 And the first one, strategy number sewn in the 00:04:24.399 --> 00:04:27.540 source material, is context -aware chunking. 00:04:28.000 --> 00:04:30.680 This directly tackles that paper shredder problem. 00:04:30.860 --> 00:04:33.420 Instead of just blindly chopping text every, 00:04:33.480 --> 00:04:37.680 say, 512 tokens, you chunk intelligently, you 00:04:37.680 --> 00:04:40.300 respect the document's structure paragraph breaks, 00:04:40.579 --> 00:04:43.420 section headings, maybe even bullet points. That 00:04:43.420 --> 00:04:45.079 seems so fundamental. It feels like it should 00:04:45.079 --> 00:04:49.519 be table stakes for any serious RG system. Minimal 00:04:49.519 --> 00:04:51.980 effort during the initial data processing, the 00:04:51.980 --> 00:04:54.699 indexing phase. Yeah, relatively minimal upfront 00:04:54.699 --> 00:04:57.620 effort. But... It ensures that when you retrieve 00:04:57.620 --> 00:05:00.259 a chunk, it's actually a complete thought, a 00:05:00.259 --> 00:05:02.420 coherent piece of information. Exactly. It pays 00:05:02.420 --> 00:05:04.759 huge dividends down the line. Though, you know, 00:05:04.779 --> 00:05:06.699 we do sometimes see pushback because it does 00:05:06.699 --> 00:05:09.439 add a little complexity to that initial data 00:05:09.439 --> 00:05:12.220 processing pipeline, the ETL. It's an extra step. 00:05:12.339 --> 00:05:15.060 Yeah. It's a necessary headache, maybe, but still 00:05:15.060 --> 00:05:16.819 a headache for some teams. All right. What's 00:05:16.819 --> 00:05:19.120 next in the baseline? This is where, for me, 00:05:19.160 --> 00:05:21.560 it gets really interesting. Strategy one, re 00:05:21.560 --> 00:05:24.980 -ranking. The source calls this the easiest win, 00:05:25.220 --> 00:05:28.100 highest ROI for lowest effort. Oh, absolutely. 00:05:28.439 --> 00:05:30.899 Re -ranking is fantastic. It's a clever two -step 00:05:30.899 --> 00:05:33.639 process that balances speed and accuracy. How 00:05:33.639 --> 00:05:37.000 does it work? So first, you do your standard, 00:05:37.079 --> 00:05:40.000 fast, broad semantic search. But instead of grabbing 00:05:40.000 --> 00:05:42.399 just the top three or five, you grab more candidates, 00:05:42.620 --> 00:05:46.399 maybe 20, maybe 50, cast a wider net initially. 00:05:46.699 --> 00:05:48.540 Okay, so you get a bigger pool of potential answers. 00:05:48.759 --> 00:05:51.480 Right. Then you take that smaller pool of candidates, 00:05:51.720 --> 00:05:54.889 say 50. chunks, and you use a second, different 00:05:54.889 --> 00:05:57.329 kind of model. This one is slower, but much, 00:05:57.350 --> 00:05:59.470 much smarter at judging relevance. It's often 00:05:59.470 --> 00:06:01.829 called a cross -encoder. A cross -encoder. So 00:06:01.829 --> 00:06:04.870 it rescores just those top 50. Precisely. It 00:06:04.870 --> 00:06:06.850 looks at the query and each candidate chunk together 00:06:06.850 --> 00:06:09.290 and gives a much more nuanced score of how well 00:06:09.290 --> 00:06:11.490 that chunk actually answers that specific question. 00:06:11.990 --> 00:06:14.370 Then you take the top, say, five from that re 00:06:14.370 --> 00:06:17.490 -ranked list. Wait, hold on. If that cross -encoder 00:06:17.490 --> 00:06:19.839 is so much smarter... Why not just use it for 00:06:19.839 --> 00:06:21.519 the initial search across the whole database? 00:06:21.779 --> 00:06:24.660 Why the two steps? What's the catch? Ah, the 00:06:24.660 --> 00:06:27.759 catch is computational cost and latency. That 00:06:27.759 --> 00:06:30.519 cross -encoder is slower and way more expensive 00:06:30.519 --> 00:06:32.699 to run because it does that detailed comparison 00:06:32.699 --> 00:06:35.600 of the query against each chunk. I see. Trying 00:06:35.600 --> 00:06:37.959 to run that super detailed comparison across 00:06:37.959 --> 00:06:40.740 potentially millions or billions of chunks in 00:06:40.740 --> 00:06:43.019 your whole knowledge base, it would take forever 00:06:43.019 --> 00:06:45.600 and cost a fortune. Okay, okay. So the first 00:06:45.600 --> 00:06:48.279 step is fast and cheap to narrow it down. Second 00:06:48.279 --> 00:06:51.139 is slow and smart for the final selection. Exactly. 00:06:51.420 --> 00:06:54.220 Wide net first, then precise judgment. That's 00:06:54.220 --> 00:06:56.680 why it's such a big win. So why is re -ranking 00:06:56.680 --> 00:06:59.060 considered non -negotiable then? Because it beautifully 00:06:59.060 --> 00:07:01.720 balances that initial search speed with much 00:07:01.720 --> 00:07:04.620 higher final accuracy. Best of both worlds, really. 00:07:04.779 --> 00:07:07.300 Makes sense. What's the third piece of this baseline 00:07:07.300 --> 00:07:10.699 stack? Third baseline fix is strategy five. Query 00:07:10.699 --> 00:07:13.360 expansion. This one really helps deal with those 00:07:13.360 --> 00:07:16.100 vague or just poorly phrased user questions we 00:07:16.100 --> 00:07:18.259 talked about. Super common in things like customer 00:07:18.259 --> 00:07:20.660 support bots. Right. How does that work? Does 00:07:20.660 --> 00:07:24.139 the system just guess related terms? Kinda, but 00:07:24.139 --> 00:07:27.600 it uses an LLM to do it smartly. The system takes 00:07:27.600 --> 00:07:30.560 the user's simple query, like reset password. 00:07:31.610 --> 00:07:34.389 And it uses an LLM to brainstorm related searches. 00:07:34.610 --> 00:07:37.129 Things like account recovery steps, forgotten 00:07:37.129 --> 00:07:40.250 password help, changed logging credentials, maybe 00:07:40.250 --> 00:07:42.870 even common misspellings. Ah, so it runs multiple 00:07:42.870 --> 00:07:45.259 searches in parallel. Based on these expanded 00:07:45.259 --> 00:07:47.699 terms. Exactly. It anticipates the different 00:07:47.699 --> 00:07:50.259 ways a user might phrase the same underlying 00:07:50.259 --> 00:07:53.600 need. It catches variations in vocabulary, jargon 00:07:53.600 --> 00:07:56.480 levels, all that stuff. Hugely valuable for improving 00:07:56.480 --> 00:07:58.399 recall, making sure you find relevant stuff, 00:07:58.579 --> 00:08:01.560 even if the user's wording isn't perfect. Okay. 00:08:01.600 --> 00:08:03.959 That baseline stack context to where chunking, 00:08:03.959 --> 00:08:06.779 re -ranking, query expansion seems really solid. 00:08:07.040 --> 00:08:10.250 High impact. relatively low complexity compared 00:08:10.250 --> 00:08:11.949 to what comes next, I imagine. That's right. 00:08:12.029 --> 00:08:14.389 Those three should probably be an 80 % or more 00:08:14.389 --> 00:08:16.649 of production R -reg systems. Now we move into 00:08:16.649 --> 00:08:19.209 the medium cost, medium complexity solutions. 00:08:19.410 --> 00:08:21.790 These start tackling more specific thorny problems, 00:08:21.910 --> 00:08:23.949 but yeah, they cost more, either in compute time 00:08:23.949 --> 00:08:26.329 or setup effort. Let's hear them. All right. 00:08:26.350 --> 00:08:30.149 First up in this tier is strategy four, contextual 00:08:30.149 --> 00:08:33.549 retrieval. This is interesting. Instead of just 00:08:33.549 --> 00:08:36.679 improving the search, This one enhances the chunks 00:08:36.679 --> 00:08:38.940 themselves during that initial indexing phase. 00:08:39.240 --> 00:08:41.919 Enhances the chunks, how? So when you're first 00:08:41.919 --> 00:08:43.779 processing your documents and creating those 00:08:43.779 --> 00:08:46.539 chunks, you don't just index the text of the 00:08:46.539 --> 00:08:49.659 chunk itself. You also use an LLM to generate 00:08:49.659 --> 00:08:52.399 a brief summary of the text immediately surrounding 00:08:52.399 --> 00:08:54.879 that chunk, the sentences before and after it. 00:08:55.080 --> 00:08:56.980 Ah, I see. So the chunk carries a little bit 00:08:56.980 --> 00:08:58.679 of its original neighborhood with it. Exactly. 00:08:58.799 --> 00:09:01.019 So maybe you have a chunk that's just the sentence. 00:09:01.279 --> 00:09:05.039 The acquisition closed in Q4. During indexing, 00:09:05.039 --> 00:09:07.000 you generate a little summary of the paragraph 00:09:07.000 --> 00:09:10.279 it came from, like, this passage discusses TechCore's 00:09:10.279 --> 00:09:13.659 2024 acquisition of data systems. And you store 00:09:13.659 --> 00:09:16.480 that summary along with the chunk's vector. Okay, 00:09:16.559 --> 00:09:18.600 that makes a lot of sense. You pay a higher cost 00:09:18.600 --> 00:09:21.539 once up front during indexing because you're 00:09:21.539 --> 00:09:23.860 running an extra LLM call for every single chunk. 00:09:23.919 --> 00:09:25.840 Right, it's a one -time cost per chunk. But then 00:09:25.840 --> 00:09:27.980 forever after, when you retrieve that chunk, 00:09:28.120 --> 00:09:31.210 it comes with richer context. The search itself 00:09:31.210 --> 00:09:33.529 might even use that summary. I could see how 00:09:33.529 --> 00:09:35.230 that would really help, especially for dense 00:09:35.230 --> 00:09:38.049 documents where context is everything. For high 00:09:38.049 --> 00:09:40.750 -value, relatively static knowledge bases, that 00:09:40.750 --> 00:09:42.649 seems like a worthwhile investment. Definitely. 00:09:42.649 --> 00:09:44.669 It provides much better context to the final 00:09:44.669 --> 00:09:47.990 generation step. Okay, next up, strategy two, 00:09:48.629 --> 00:09:51.700 agentic RG. Now, this is where the complexity 00:09:51.700 --> 00:09:55.720 really starts to ramp up. Agentic RG sounds sophisticated. 00:09:56.139 --> 00:09:58.240 It is. Instead of just running that single linear 00:09:58.240 --> 00:10:00.860 pipeline retrieve, then generally use an agent. 00:10:00.960 --> 00:10:03.899 An agent is basically an LLM tasked with reasoning 00:10:03.899 --> 00:10:06.600 about the user's query and planning a sequence 00:10:06.600 --> 00:10:08.620 of actions. And so it doesn't just search once. 00:10:08.700 --> 00:10:11.340 It plans multiple steps. Correct. Think back 00:10:11.340 --> 00:10:14.360 to that Q3 performance question. A basic RG might 00:10:14.360 --> 00:10:17.259 just search the sales reports. An agentic RG 00:10:17.259 --> 00:10:20.460 might reason. Okay, to answer about Q3 performance 00:10:20.460 --> 00:10:22.840 comprehensively, I need to first check the financial 00:10:22.840 --> 00:10:25.419 database for revenue and profit figures. Then 00:10:25.419 --> 00:10:27.299 I need to check the sales reports for regional 00:10:27.299 --> 00:10:29.600 breakdowns. And then I should check the customer 00:10:29.600 --> 00:10:32.370 feedback summaries for sentiment analysis. Whoa. 00:10:32.490 --> 00:10:35.710 So it orchestrates a multi -step, multi -source 00:10:35.710 --> 00:10:39.289 search strategy. Precisely. It breaks the problem 00:10:39.289 --> 00:10:41.950 down, executes the steps, maybe even synthesizes 00:10:41.950 --> 00:10:44.809 the findings from different sources. It's incredibly 00:10:44.809 --> 00:10:47.710 powerful for complex questions that require pulling 00:10:47.710 --> 00:10:50.919 information from multiple places. But there's 00:10:50.919 --> 00:10:53.419 always a but. It's a nightmare to build and debug 00:10:53.419 --> 00:10:56.159 reliably. Honestly, I still wrestle with prompt 00:10:56.159 --> 00:10:59.080 drift myself when I'm debugging these agent chains. 00:10:59.360 --> 00:11:02.179 It's tough. Prompt drift. What do you mean by 00:11:02.179 --> 00:11:04.559 that? Does the agent just forget what it's doing 00:11:04.559 --> 00:11:06.500 halfway through? Can you give an example of a 00:11:06.500 --> 00:11:08.470 failure you've seen? Yeah, it's kind of like 00:11:08.470 --> 00:11:10.529 that, or it gets stuck in loops, or its reasoning 00:11:10.529 --> 00:11:13.549 goes off the rails. The worst I saw recently 00:11:13.549 --> 00:11:16.309 was a circular dependency it created for itself. 00:11:16.570 --> 00:11:19.149 It was supposed to check document A, then document 00:11:19.149 --> 00:11:21.970 B, then combine facts. Okay. But based on some 00:11:21.970 --> 00:11:24.350 subtle nuance it picked up from document A and 00:11:24.350 --> 00:11:26.330 maybe its internal state from a previous turn, 00:11:26.470 --> 00:11:29.450 it decided document B contradicted A, even though 00:11:29.450 --> 00:11:31.830 it didn't really, hallucinated a reason why checking 00:11:31.830 --> 00:11:34.269 B was unnecessary, and then just skipped it entirely. 00:11:34.529 --> 00:11:36.830 And the whole time it outputted this perfect... 00:11:36.840 --> 00:11:39.279 logical sounding step -by -step reasoning for 00:11:39.279 --> 00:11:42.620 why I was skipping B. Debugging the agent's reasoning 00:11:42.620 --> 00:11:45.620 process is so much harder than debugging a simple 00:11:45.620 --> 00:11:47.820 linear pipeline where data just flows from A 00:11:47.820 --> 00:11:50.139 to B to C. That sounds incredibly frustrating. 00:11:50.679 --> 00:11:53.700 So how do we avoid over -engineering with something 00:11:53.700 --> 00:11:56.399 like an agent? When should we actually use it? 00:11:56.600 --> 00:12:00.110 My advice. Skip agents entirely unless your users' 00:12:00.230 --> 00:12:02.610 questions genuinely require that kind of multi 00:12:02.610 --> 00:12:05.090 -source lookup and multi -step reasoning. If 00:12:05.090 --> 00:12:08.009 a simple retrieve then generate works 90 % of 00:12:08.009 --> 00:12:10.509 the time, adding an agent is probably overkill 00:12:10.509 --> 00:12:12.629 and introduces more problems than it solves. 00:12:12.889 --> 00:12:15.610 Use it only when the complexity is truly warranted. 00:12:15.730 --> 00:12:18.129 Okay, that's a crucial reality check. So we've 00:12:18.129 --> 00:12:20.090 covered baseline. We've covered medium complexity. 00:12:20.389 --> 00:12:22.129 What about the really high -stakes situations? 00:12:22.669 --> 00:12:25.169 Medical diagnosis aids, financial compliance 00:12:25.169 --> 00:12:27.909 checkers, legal discovery. Places where getting 00:12:27.909 --> 00:12:30.870 it wrong is really bad. Right. Now we're into 00:12:30.870 --> 00:12:33.929 the heavy -duty, specialized techniques. These 00:12:33.929 --> 00:12:36.169 often come with significant costs, usually in 00:12:36.169 --> 00:12:38.250 latency or computation, but they're designed 00:12:38.250 --> 00:12:41.289 for maximum accuracy and reliability. Strategy 00:12:41.289 --> 00:12:44.509 10 is self -reflective ROG. Self -reflective? 00:12:44.870 --> 00:12:48.240 The AI checks its own work. Pretty much. This 00:12:48.240 --> 00:12:50.679 is a direct assault on hallucinations and incomplete 00:12:50.679 --> 00:12:53.720 answers. After the LLM generates its initial 00:12:53.720 --> 00:12:56.840 response, answer V1, the system doesn't just 00:12:56.840 --> 00:13:00.240 return it. It forces the same LLM, or maybe another 00:13:00.240 --> 00:13:03.179 one, to critique that answer. It asks specific 00:13:03.179 --> 00:13:05.980 questions like, does this response fully address 00:13:05.980 --> 00:13:09.080 the user's original question? Is every statement 00:13:09.080 --> 00:13:11.779 in this response directly supported by the retrieved 00:13:11.779 --> 00:13:14.620 source documents? Are there any unsupported claims? 00:13:14.980 --> 00:13:17.480 Wow. And what happens if the critique finds flaws? 00:13:17.740 --> 00:13:20.679 If the AI self -critic says, no, this answer 00:13:20.679 --> 00:13:23.039 is incomplete or this claim isn't supported, 00:13:23.340 --> 00:13:25.860 the system forces it to regenerate the answer, 00:13:25.919 --> 00:13:28.179 taking the critique into account. It iterates, 00:13:28.179 --> 00:13:30.960 creating answer V2, maybe even V3, until the 00:13:30.960 --> 00:13:32.840 answer passes the self -check. That's potentially 00:13:32.840 --> 00:13:35.460 huge for accuracy. But the trade -off must be 00:13:35.460 --> 00:13:37.759 cost and speed, right? You're basically running 00:13:37.759 --> 00:13:40.120 the LLM generation step two or three times per 00:13:40.120 --> 00:13:43.100 query. Exactly. It can easily double or triple 00:13:43.100 --> 00:13:45.779 your LLM costs and latency per query. It's a 00:13:45.779 --> 00:13:48.679 very direct trade -off. Are you willing to pay 00:13:48.679 --> 00:13:50.919 significantly more for each answer to get that 00:13:50.919 --> 00:13:53.500 extra layer of verification? For high -stakes 00:13:53.500 --> 00:13:56.080 applications, the answer might be yes. For a 00:13:56.080 --> 00:14:00.409 casual chatbot, probably not. versus cost and 00:14:00.409 --> 00:14:03.750 speed, a classic engineering dilemma. What else 00:14:03.750 --> 00:14:06.090 is in this high stakes category? Strategy nine 00:14:06.090 --> 00:14:09.309 is hierarchical rag. This one is aimed squarely 00:14:09.309 --> 00:14:11.509 at dealing with truly massive document collections. 00:14:11.929 --> 00:14:14.960 Think millions, maybe billions of pages, like 00:14:14.960 --> 00:14:17.960 a giant legal archive or a comprehensive scientific 00:14:17.960 --> 00:14:20.399 library. Okay. How does hierarchy help there? 00:14:20.480 --> 00:14:22.299 Instead of just chunking everything into small 00:14:22.299 --> 00:14:25.200 pieces, you store information at multiple levels 00:14:25.200 --> 00:14:27.360 of granularity simultaneously. You might have 00:14:27.360 --> 00:14:30.299 the full text of a document, but also pre -generated 00:14:30.299 --> 00:14:32.440 chapter summaries, section summaries, maybe even 00:14:32.440 --> 00:14:34.879 paragraph summaries, all indexed. So you have 00:14:34.879 --> 00:14:37.610 different zoom levels of the information. Exactly. 00:14:37.850 --> 00:14:40.870 When a query comes in, the system can be smart 00:14:40.870 --> 00:14:43.309 about where to search first. A broad, high -level 00:14:43.309 --> 00:14:46.269 query. Maybe it just searches the chapter summaries 00:14:46.269 --> 00:14:48.769 first. That's much faster than searching millions 00:14:48.769 --> 00:14:51.970 of tiny chunks. A very specific, detailed query. 00:14:52.269 --> 00:14:55.029 Okay, then it drills down to searching the individual 00:14:55.029 --> 00:14:57.590 chunks within the relevant sections. It searches 00:14:57.590 --> 00:15:00.009 the appropriate level of detail. Saves a ton 00:15:00.009 --> 00:15:02.809 of computation on broad queries, I imagine. But 00:15:02.809 --> 00:15:06.360 that sounds like a nightmare to manage. keeping 00:15:06.360 --> 00:15:10.340 all those summaries perfectly in sync when the 00:15:10.340 --> 00:15:12.980 underlying source documents get updated. It must 00:15:12.980 --> 00:15:15.259 be a huge challenge, right? Oh, it's a massive 00:15:15.259 --> 00:15:17.179 indexing and maintenance challenge. You absolutely 00:15:17.179 --> 00:15:19.679 need sophisticated tooling and really careful 00:15:19.679 --> 00:15:22.000 pipeline orchestration to keep that hierarchy 00:15:22.000 --> 00:15:24.299 consistent. It's not trivial. But the payoff? 00:15:24.539 --> 00:15:27.019 The payoff can be huge for performance at scale. 00:15:27.080 --> 00:15:29.299 I mean, whoa, imagine scaling this to handle 00:15:29.299 --> 00:15:31.940 like a billion queries a day across an entire 00:15:31.940 --> 00:15:34.399 national archive or something. That level of 00:15:34.399 --> 00:15:36.820 nested detail combined with the efficiency, it's 00:15:36.820 --> 00:15:39.179 pretty incredible what becomes possible. Yeah, 00:15:39.259 --> 00:15:41.970 the scale is mind -boggling. Okay. Is there one 00:15:41.970 --> 00:15:44.450 more? You mentioned 11 strategies. There is. 00:15:44.610 --> 00:15:47.809 The final one, strategy 11, often considered 00:15:47.809 --> 00:15:51.110 the expert mode or the final boss of RAG optimization, 00:15:51.710 --> 00:15:55.250 fine -tuned embeddings. Fine -tuning the embeddings 00:15:55.250 --> 00:15:57.450 themselves. Right. Not just the LLM, but the 00:15:57.450 --> 00:16:00.669 model that creates those vector addresses. Precisely. 00:16:00.669 --> 00:16:03.570 So instead of using a general purpose embedding 00:16:03.570 --> 00:16:06.450 model that was trained on like the whole Internet, 00:16:06.649 --> 00:16:08.789 you take that model and you continue training 00:16:08.789 --> 00:16:11.429 it. You fine tune it specifically on your documents 00:16:11.429 --> 00:16:14.789 with your domain specific jargon and nuances. 00:16:15.049 --> 00:16:17.490 You teach it what acronyms mean in your context. 00:16:17.629 --> 00:16:21.029 Exactly. You teach it that. MI means myocardial 00:16:21.029 --> 00:16:22.889 infarction in your medical documents, but it 00:16:22.889 --> 00:16:25.710 means Michigan or management information in your 00:16:25.710 --> 00:16:28.049 logistics database. The general model might get 00:16:28.049 --> 00:16:30.149 confused, but a fine -tuned model learns the 00:16:30.149 --> 00:16:32.509 specific language of your world. That sounds 00:16:32.509 --> 00:16:35.529 incredibly powerful for specialized fields, but 00:16:35.529 --> 00:16:39.149 also expensive and difficult. Very. You need 00:16:39.149 --> 00:16:41.169 a high -quality data set for training, which 00:16:41.169 --> 00:16:43.570 can be hard to create. You need significant machine 00:16:43.570 --> 00:16:46.450 learning expertise on your team. And you need 00:16:46.450 --> 00:16:48.950 the computational resources for the fine -tuning 00:16:48.950 --> 00:16:52.269 itself. It's generally only worth it for large 00:16:52.269 --> 00:16:54.850 organizations with really unique, high -value 00:16:54.850 --> 00:16:58.070 knowledge domains, where the general models just 00:16:58.070 --> 00:16:59.789 fundamentally misunderstand the terminology. 00:17:00.210 --> 00:17:02.409 Okay, wow. That's a lot of ground covered. From 00:17:02.409 --> 00:17:05.869 simple fixes to highly complex, specialized solutions. 00:17:07.400 --> 00:17:09.240 bring it back to the listener. If you're building 00:17:09.240 --> 00:17:12.059 production in our Regie system today, what's 00:17:12.059 --> 00:17:14.720 the takeaway? You clearly don't need all 11 strategies. 00:17:15.059 --> 00:17:17.740 How do you choose? That's the absolute key takeaway. 00:17:17.920 --> 00:17:20.180 You need a strategic stack, not just a grab bag 00:17:20.180 --> 00:17:23.220 of techniques. For probably 80 % of applications 00:17:23.220 --> 00:17:25.680 out there, that baseline stack we discussed is 00:17:25.680 --> 00:17:27.279 going to be your workhorse and give you the best 00:17:27.279 --> 00:17:30.769 ROI. So start with... Context -aware chunking. 00:17:30.769 --> 00:17:32.869 Yep. Re -ranking. Definitely. And query expansion. 00:17:33.109 --> 00:17:35.470 Get those three right first. They address the 00:17:35.470 --> 00:17:38.230 most common and impactful failure modes of naive 00:17:38.230 --> 00:17:42.269 RE. And then only add the heavier, more complex 00:17:42.269 --> 00:17:45.490 solutions like agentic ROG or self -reflection 00:17:45.490 --> 00:17:48.710 or fine -tuning if and only if you've clearly 00:17:48.710 --> 00:17:50.829 measured a specific failure point in your system 00:17:50.829 --> 00:17:53.170 and you know that one of these advanced techniques 00:17:53.170 --> 00:17:55.049 is specifically designed to fix that problem. 00:17:55.769 --> 00:17:58.309 Don't add complexity for complexity's sake. That 00:17:58.309 --> 00:18:00.670 leads perfectly into the traps to avoid. The 00:18:00.670 --> 00:18:03.049 common mistakes people make when trying to improve 00:18:03.049 --> 00:18:05.410 their RJAG systems. What's the first big one? 00:18:05.670 --> 00:18:08.730 Trap number one, over -engineering on day one. 00:18:09.369 --> 00:18:11.950 Just like we said, don't try to build the Starship 00:18:11.950 --> 00:18:14.170 Enterprise when a reliable shuttlecraft will 00:18:14.170 --> 00:18:17.230 do. Don't implement all 11 strategies right out 00:18:17.230 --> 00:18:19.829 of the gate. Start simple. Get that baseline 00:18:19.829 --> 00:18:22.369 working well, especially add re -ranking early. 00:18:22.470 --> 00:18:24.529 It's such a big win. And then iterate based on 00:18:24.529 --> 00:18:26.349 observed problems. Makes total sense. What's 00:18:26.349 --> 00:18:28.369 trap number two? Trap two, ignoring evaluation, 00:18:28.809 --> 00:18:31.190 or as the source material nicely puts it, flying 00:18:31.190 --> 00:18:33.470 blind. You absolutely cannot improve what you 00:18:33.470 --> 00:18:36.849 don't measure. You need metrics. The best practice 00:18:36.849 --> 00:18:39.160 is to create a gold standard evaluation. evaluation 00:18:39.160 --> 00:18:43.480 set. Maybe it's 20, 30, 50 really hard representative 00:18:43.480 --> 00:18:46.480 questions where you know what the correct answer 00:18:46.480 --> 00:18:49.400 should be based on your documents. Like a final 00:18:49.400 --> 00:18:52.380 exam for your RG system. Exactly. And you run 00:18:52.380 --> 00:18:54.299 your system against that test set before you 00:18:54.299 --> 00:18:56.759 make a change and after you make a change. Did 00:18:56.759 --> 00:18:59.400 adding contextual retrieval actually improve 00:18:59.400 --> 00:19:02.200 the score on your hard questions? Did implementing 00:19:02.200 --> 00:19:04.400 self -reflection reduce hallucinations on that 00:19:04.400 --> 00:19:06.740 specific set? Without that data, you're just 00:19:06.740 --> 00:19:08.940 guessing. You need objective proof that you're 00:19:08.940 --> 00:19:11.279 changing. are actually helping. Yeah. Crucial. 00:19:11.339 --> 00:19:13.740 Okay. And the third trap. This one seems particularly 00:19:13.740 --> 00:19:16.339 important for user -facing applications. Yeah, 00:19:16.400 --> 00:19:18.539 the third trap is critical. Forgetting about 00:19:18.539 --> 00:19:21.740 latency. A super smart, incredibly accurate RG 00:19:21.740 --> 00:19:24.759 system is completely useless if it takes 30 seconds 00:19:24.759 --> 00:19:26.440 to give the user an answer. People just won't 00:19:26.440 --> 00:19:29.140 wait. They won't, especially in interactive applications 00:19:29.140 --> 00:19:32.619 like chatbots or customer support tools. You 00:19:32.619 --> 00:19:34.740 have to consider the speed implications of each 00:19:34.740 --> 00:19:37.680 strategy. We talked about self -reflective ARGI, 00:19:37.799 --> 00:19:40.480 potentially doubling or tripling latency that 00:19:40.480 --> 00:19:42.640 might be totally unacceptable for a real -time 00:19:42.640 --> 00:19:44.920 conversation. So you need to match the strategy 00:19:44.920 --> 00:19:47.380 not just to the accuracy requirement, but also 00:19:47.380 --> 00:19:50.779 to the user's expectation of speed. If latency 00:19:50.779 --> 00:19:53.519 is your biggest problem, maybe you avoid self 00:19:53.519 --> 00:19:55.559 -reflection and instead focus on things like 00:19:55.559 --> 00:19:58.440 re -ranking or hierarchical RAG that can sometimes 00:19:58.440 --> 00:20:01.220 speed up retrieval. Precisely. It's always a 00:20:01.220 --> 00:20:04.619 balancing act. quality, cost, speed. You have 00:20:04.619 --> 00:20:06.480 to optimize for the specific constraints and 00:20:06.480 --> 00:20:09.079 goals of your application. Build a reliable system 00:20:09.079 --> 00:20:12.180 that solves the user's actual problem, not necessarily 00:20:12.180 --> 00:20:14.779 a theoretical masterpiece of engineering complexity. 00:20:15.299 --> 00:20:18.700 So ultimately, the journey from a basic, brittle 00:20:18.700 --> 00:20:22.259 R key to a great production -ready one, it isn't 00:20:22.259 --> 00:20:24.019 really about piling on more and more complex 00:20:24.019 --> 00:20:26.970 features, is it? Not at all. It's about the strategic 00:20:26.970 --> 00:20:29.269 combination of the right features for your specific 00:20:29.269 --> 00:20:31.730 needs and having a clear -eyed view of the trade 00:20:31.730 --> 00:20:33.650 -offs. It's about choosing your tools wisely. 00:20:34.130 --> 00:20:37.190 Exactly. Because knowledge ultimately is most 00:20:37.190 --> 00:20:39.349 valuable when it's understood and applied correctly. 00:20:39.849 --> 00:20:42.549 The goal is a system that delivers reliable value 00:20:42.549 --> 00:20:45.029 consistently. That's a great place to summarize. 00:20:45.170 --> 00:20:48.349 Build for value, not just for technical sophistication. 00:20:48.720 --> 00:20:51.420 Before we wrap up, though, one final thought 00:20:51.420 --> 00:20:53.720 to leave our listeners with. We talked about 00:20:53.720 --> 00:20:56.259 self -reflective ARGOG, where the AI critically 00:20:56.259 --> 00:20:59.039 examines its own answer against the source material 00:20:59.039 --> 00:21:01.859 to catch errors or fabrications. Yeah, a powerful 00:21:01.859 --> 00:21:05.339 technique. But here's the question. What if the 00:21:05.339 --> 00:21:08.680 underlying source data itself, the documents 00:21:08.680 --> 00:21:11.539 you fed into the system, what if that data is 00:21:11.539 --> 00:21:13.980 incomplete? or maybe it contains inherent biases. 00:21:14.420 --> 00:21:17.519 Can any amount of sophisticated AI self -reflection 00:21:17.519 --> 00:21:20.579 after the fact truly save the final answer if 00:21:20.579 --> 00:21:22.460 the foundation it's built on is flawed to begin 00:21:22.460 --> 00:21:25.170 with? beat, that's something to really mull over 00:21:25.170 --> 00:21:27.970 as you build and, just as importantly, as you 00:21:27.970 --> 00:21:30.450 evaluate the trustworthiness of your own AI systems. 00:21:30.730 --> 00:21:32.690 That's a profound point about data integrity 00:21:32.690 --> 00:21:34.970 being the bedrock, a really important consideration. 00:21:35.329 --> 00:21:37.009 Well, thank you for joining us on this deep dive 00:21:37.009 --> 00:21:39.369 into Advanced R -GRAG. We really hope exploring 00:21:39.369 --> 00:21:41.329 these strategies helps you navigate the path 00:21:41.329 --> 00:21:43.910 from that initial demo frustration towards building 00:21:43.910 --> 00:21:46.150 genuinely robust and valuable AI applications. 00:21:46.670 --> 00:21:47.569 Until next time.