WEBVTT 00:00:00.000 --> 00:00:02.980 Welcome to the Deep Dive. Today we're tackling 00:00:02.980 --> 00:00:06.519 a concept that's, well, it's rapidly becoming 00:00:06.519 --> 00:00:08.599 fundamental if you're serious about making AI 00:00:08.599 --> 00:00:12.320 work in the real world. Retrieval augmented generation. 00:00:12.640 --> 00:00:15.919 Our rag for short. Yep our rag we were looking 00:00:15.919 --> 00:00:18.460 at some material an article really that lays 00:00:18.460 --> 00:00:20.679 it out pretty clearly it explains what it is 00:00:20.679 --> 00:00:22.899 and Maybe more importantly why it's such a big 00:00:22.899 --> 00:00:24.960 deal right now, right? Because everyone's seen 00:00:24.960 --> 00:00:27.460 what these large language models can do, you 00:00:27.460 --> 00:00:31.760 know the GPT's Paul M Gemini llama Claude They're 00:00:31.760 --> 00:00:34.439 amazing with language totally phenomenal generating 00:00:34.439 --> 00:00:37.259 text understanding it But and this is what the 00:00:37.259 --> 00:00:39.460 source material we looked at really points out 00:00:39.460 --> 00:00:42.759 these models powerful as they are They have some 00:00:42.759 --> 00:00:45.140 built -in limits, like right out of the box. 00:00:45.399 --> 00:00:47.600 OK, let's unpack this then, because they seem 00:00:47.600 --> 00:00:49.740 so smart. What are these constraints? What does 00:00:49.740 --> 00:00:51.780 the article actually highlight? Well, the text 00:00:51.780 --> 00:00:53.920 is pretty direct about the main problems. First 00:00:53.920 --> 00:00:56.200 off, their knowledge is basically frozen in time. 00:00:56.520 --> 00:00:58.479 Frozen? Yeah, static. It's limited to whatever 00:00:58.479 --> 00:01:00.960 massive data sets they were trained on. So if 00:01:00.960 --> 00:01:02.820 something happened after that training cutoff 00:01:02.820 --> 00:01:06.359 date, the model just doesn't know about it. Oh, 00:01:06.359 --> 00:01:09.980 OK. So asking about like market news from yesterday 00:01:09.980 --> 00:01:12.909 or some brand new research. It won't have the 00:01:12.909 --> 00:01:15.129 latest. Exactly. It's completely unaware. And 00:01:15.129 --> 00:01:16.950 the second big one, especially if you're thinking 00:01:16.950 --> 00:01:20.689 about using this in a business, is that they 00:01:20.689 --> 00:01:25.090 can't access private information. Nonpublic stuff. 00:01:25.590 --> 00:01:27.650 Proprietary company knowledge. Right. Internal 00:01:27.650 --> 00:01:31.349 reports, databases, specific customer info, that 00:01:31.349 --> 00:01:33.180 kind of thing. Precisely. It wasn't in the public 00:01:33.180 --> 00:01:35.799 training data, so the LLM can't see it, can't 00:01:35.799 --> 00:01:37.819 answer questions based on your internal documents 00:01:37.819 --> 00:01:39.840 or processes. So you can't just ask it, hey, 00:01:40.019 --> 00:01:41.799 what were the key findings from last quarter's 00:01:41.799 --> 00:01:44.079 internal review? Nope, not the base model. And 00:01:44.079 --> 00:01:45.760 then there's the third issue the source talks 00:01:45.760 --> 00:01:47.939 about, hallucinations. Right, I've heard that 00:01:47.939 --> 00:01:50.859 term. They just make things up. Sort of. They're 00:01:50.859 --> 00:01:53.400 designed to generate text that sounds plausible 00:01:53.400 --> 00:01:56.099 based on patterns they learned. But sometimes 00:01:56.099 --> 00:01:58.659 that leads them to state incorrect information 00:01:58.659 --> 00:02:02.340 very, very confidently. It sounds totally convincing, 00:02:02.680 --> 00:02:05.319 but it's factually wrong. Which is obviously 00:02:05.319 --> 00:02:08.219 a huge problem if you need accuracy, especially 00:02:08.219 --> 00:02:11.759 with specific current or sensitive internal data. 00:02:12.599 --> 00:02:15.000 So, okay, these are pretty big limitations. How 00:02:15.000 --> 00:02:17.560 do we actually make these models useful for those 00:02:17.560 --> 00:02:20.319 kinds of tasks? Is this where RAG comes in? This 00:02:20.319 --> 00:02:22.860 is exactly where the article positions our RAG. 00:02:23.080 --> 00:02:25.620 It's presented as a really practical, pretty 00:02:25.620 --> 00:02:28.080 powerful way to breach those exact gaps we just 00:02:28.080 --> 00:02:31.319 talked about. Ah, okay, giving the LLMs. the 00:02:31.319 --> 00:02:33.400 context they're missing. That's the core idea, 00:02:33.400 --> 00:02:35.180 yeah. Okay, here's where it gets really interesting 00:02:35.180 --> 00:02:39.240 then. If RGAG is the solution, what is it? What 00:02:39.240 --> 00:02:42.039 does the source material say retrieval augmented 00:02:42.039 --> 00:02:45.120 generation actually means? So the text describes 00:02:45.120 --> 00:02:49.219 RGAG basically as a technique, a method to seriously 00:02:49.219 --> 00:02:51.419 boost the quality and relevance of a language 00:02:51.419 --> 00:02:54.370 model's output. And the key thing is it does 00:02:54.370 --> 00:02:57.090 this by feeding the model extra relevant data 00:02:57.090 --> 00:02:59.430 along with the user's input without having to 00:02:59.430 --> 00:03:02.009 retrain or change the underlying LM itself. So 00:03:02.009 --> 00:03:03.990 you're giving it extra info on the fly. Pretty 00:03:03.990 --> 00:03:06.169 much. And that extra info, that supplemental 00:03:06.169 --> 00:03:08.430 data, it can come from external sources, things 00:03:08.430 --> 00:03:11.229 that are kept up to date, or, and this is crucial 00:03:11.229 --> 00:03:13.949 for businesses, it can come from an organization's 00:03:13.949 --> 00:03:16.449 own private knowledge base, internal documents, 00:03:17.030 --> 00:03:20.150 databases, whatever. So instead of the LLM just 00:03:20.150 --> 00:03:22.569 guessing based on its old training data, you're 00:03:22.569 --> 00:03:24.750 saying, here, look at this specific information 00:03:24.750 --> 00:03:26.569 before you answer. That's a perfect way to put 00:03:26.569 --> 00:03:28.909 it. You're giving it the specific facts it needs 00:03:28.909 --> 00:03:32.490 for that particular question or task. The source 00:03:32.490 --> 00:03:36.930 explains Argue as merging two things, a pre -trained 00:03:36.930 --> 00:03:39.009 language model, that's the generator creating 00:03:39.009 --> 00:03:42.210 the text, and external knowledge index, which 00:03:42.210 --> 00:03:44.479 is handled by something called a retriever. Retriever 00:03:44.479 --> 00:03:46.840 and generator got it and the text mentions this 00:03:46.840 --> 00:03:50.419 approach came out of Facebook AI research It 00:03:50.419 --> 00:03:53.400 showed pretty quickly much better results on 00:03:53.400 --> 00:03:55.939 tasks that need a lot of specific knowledge like 00:03:55.939 --> 00:03:58.939 question answering checking facts It just generated 00:03:58.939 --> 00:04:01.599 language that was way more precise and factual 00:04:01.599 --> 00:04:04.439 Okay, so it's this combo get the right info first 00:04:04.439 --> 00:04:08.219 then generate that makes sense The article digs 00:04:08.219 --> 00:04:10.259 into the how right it talks about the architecture 00:04:10.259 --> 00:04:13.259 of these two players the retriever and the generator 00:04:13.400 --> 00:04:15.520 Let's start with the Retriever. What's its specific 00:04:15.520 --> 00:04:19.560 job? The Retriever is basically the smart search 00:04:19.560 --> 00:04:22.220 engine part of the system. Its job is to sift 00:04:22.220 --> 00:04:24.759 through potentially huge amounts of information. 00:04:25.040 --> 00:04:28.019 Could be databases inside a company, documents, 00:04:28.480 --> 00:04:30.620 maybe even stuff on the web. And the source mentioned 00:04:30.620 --> 00:04:33.910 it can connect to, like, Business systems. Yeah, 00:04:34.129 --> 00:04:36.990 exactly. Things like CRMs, ERP systems, cloud 00:04:36.990 --> 00:04:39.529 storage, other line of business apps. But its 00:04:39.529 --> 00:04:41.569 key function isn't just searching. It's about 00:04:41.569 --> 00:04:43.670 intelligently filtering and finding only the 00:04:43.670 --> 00:04:45.629 pieces of information that are most relevant 00:04:45.629 --> 00:04:48.209 to the user -specific question. Like a super 00:04:48.209 --> 00:04:50.449 -efficient librarian finding the exact paragraph 00:04:50.449 --> 00:04:53.649 you need. That's a great analogy. And that specific 00:04:53.649 --> 00:04:56.399 relevant info it digs up. That becomes the context 00:04:56.399 --> 00:04:58.519 that gets passed on. The article actually gives 00:04:58.519 --> 00:05:01.000 BingChat as an example of something using this 00:05:01.000 --> 00:05:03.860 kind of mechanism to pull in current info rather 00:05:03.860 --> 00:05:05.759 than just relying on the model's cutoff date. 00:05:06.060 --> 00:05:09.319 Got it. So the retriever fetches the relevant 00:05:09.319 --> 00:05:11.420 snippets and then the generator, which is the 00:05:11.420 --> 00:05:13.670 LLM, it takes those snippets and what? And it 00:05:13.670 --> 00:05:15.870 generates the final answer. The generator, the 00:05:15.870 --> 00:05:18.870 LLM, takes that context provided by the retriever 00:05:18.870 --> 00:05:21.250 and uses that information to craft the response 00:05:21.250 --> 00:05:24.470 in natural language. It bases its answer specifically 00:05:24.470 --> 00:05:26.709 on the facts it was just given. So it's grounded 00:05:26.709 --> 00:05:30.050 in the retrieved info. Precisely. Grounded. That 00:05:30.050 --> 00:05:32.449 ensures the answer is much more likely to be 00:05:32.449 --> 00:05:34.970 factual and directly relevant to the query and 00:05:34.970 --> 00:05:37.720 the specific context. The article also notes 00:05:37.720 --> 00:05:39.560 you can use things like prompt engineering here 00:05:39.560 --> 00:05:42.660 to further guide how the LLM uses that context, 00:05:42.740 --> 00:05:44.540 something we've touched on before. OK, that high 00:05:44.540 --> 00:05:47.339 level idea, find context, then generate based 00:05:47.339 --> 00:05:49.740 on it clicks. Can you walk us through the actual 00:05:49.740 --> 00:05:51.980 sequence, the step by step the source describes, 00:05:52.040 --> 00:05:54.060 like when I ask a question, what happens? Sure. 00:05:54.220 --> 00:05:56.259 The text lays out a pretty standard flow. So 00:05:56.259 --> 00:05:58.339 it starts when you, the user, submit a query, 00:05:58.519 --> 00:06:01.279 ask a question. First, that question, your text, 00:06:01.560 --> 00:06:03.480 gets turned into a numerical format, usually 00:06:03.480 --> 00:06:06.410 a vector. A mathematical representation. Exactly. 00:06:06.730 --> 00:06:09.970 Captures the meaning numerically. Then the Retriever 00:06:09.970 --> 00:06:13.250 component takes this vector and uses it to search. 00:06:13.810 --> 00:06:16.009 It searches through an index, usually an index 00:06:16.009 --> 00:06:18.129 of documents that have been broken down into 00:06:18.129 --> 00:06:20.449 smaller chunks. Tunks, like paragraphs or something. 00:06:20.509 --> 00:06:22.930 Yeah, smaller pieces of text. It looks for the 00:06:22.930 --> 00:06:25.689 chunks whose own vectors are mathematically closest 00:06:25.689 --> 00:06:28.730 to the query vector. That closeness means they're 00:06:28.730 --> 00:06:30.889 semantically relevant. They're talking about 00:06:30.889 --> 00:06:33.500 the same concepts as your question. Okay, so 00:06:33.500 --> 00:06:35.899 the math helps find the best matching pieces 00:06:35.899 --> 00:06:38.519 from the knowledge base. Right. And once it finds 00:06:38.519 --> 00:06:41.360 those relevant chunks, that information is combined 00:06:41.360 --> 00:06:43.800 with your original query. Together, they form 00:06:43.800 --> 00:06:46.399 what's called an augmented prompt. Augmented, 00:06:46.420 --> 00:06:48.860 so it's my question peelers at this extra context 00:06:48.860 --> 00:06:50.879 it found. Exactly. It's not just your question 00:06:50.879 --> 00:06:53.519 anymore. It's your question supercharged with 00:06:53.519 --> 00:06:55.779 relevant facts. And that whole thing gets sent 00:06:55.779 --> 00:06:58.639 to the LLM. That's the critical next step. This 00:06:58.639 --> 00:07:01.240 augmented prompt query plus context goes to the 00:07:01.240 --> 00:07:04.470 LLM. And crucially, the LLM is usually instructed 00:07:04.470 --> 00:07:07.230 to generate its response based only on the information 00:07:07.230 --> 00:07:09.670 provided right there in that prompt. Use this 00:07:09.670 --> 00:07:12.209 context. Don't just rely on your training data. 00:07:12.970 --> 00:07:16.269 Precisely. The retrieved context becomes its 00:07:16.269 --> 00:07:19.209 primary source of truth for answering your specific 00:07:19.209 --> 00:07:22.490 question. Wow, okay. That really changes the 00:07:22.490 --> 00:07:25.379 game. It's not... just drawing on its vast, but 00:07:25.379 --> 00:07:27.579 maybe outdated, internal knowledge. It's using 00:07:27.579 --> 00:07:30.660 a targeted data set you just handed it. So why 00:07:30.660 --> 00:07:32.920 is this so important, especially for businesses? 00:07:33.199 --> 00:07:35.220 The article listed some key benefits, right? 00:07:35.360 --> 00:07:38.079 Oh, absolutely. It's transformative for several 00:07:38.079 --> 00:07:40.319 reasons, particularly in an enterprise setting. 00:07:40.800 --> 00:07:43.139 A huge one is accessing up -to -date information, 00:07:43.579 --> 00:07:45.339 pulling from external sources that are current. 00:07:45.420 --> 00:07:47.579 Makes sense. What else? Another critical one 00:07:47.579 --> 00:07:50.160 is using private data. Companies can finally 00:07:50.160 --> 00:07:52.699 leverage their own internal reports, customer 00:07:52.699 --> 00:07:55.139 data, internal wikis, whatever, to ground the 00:07:55.139 --> 00:07:58.420 AI's responses. That's massive for building tools 00:07:58.420 --> 00:08:00.240 that are actually useful within that specific 00:08:00.240 --> 00:08:02.399 company. And I guess that directly tackles the 00:08:02.399 --> 00:08:04.439 hallucination problem we talked about. Exactly. 00:08:04.620 --> 00:08:06.439 That's a core benefit the article highlights. 00:08:06.980 --> 00:08:09.300 A significant reduction in hallucinations and 00:08:09.300 --> 00:08:12.279 much better factual accuracy. Because the LLM 00:08:12.279 --> 00:08:15.740 is told, often very explicitly, answer only using 00:08:15.740 --> 00:08:18.439 this context. It's less likely to just make stuff 00:08:18.439 --> 00:08:20.720 up. Right. It's answering based on verifiable 00:08:20.720 --> 00:08:23.199 sources you provided. The article also mentions 00:08:23.199 --> 00:08:25.579 just generally enhanced response quality and 00:08:25.579 --> 00:08:28.480 relevance. If the AI is grounded in specific, 00:08:28.939 --> 00:08:31.220 accurate data, the output is naturally going 00:08:31.220 --> 00:08:34.639 to be better, more diverse, more customized for 00:08:34.639 --> 00:08:37.490 what the user actually needs. And using verifiable 00:08:37.490 --> 00:08:39.789 sources. That sounds like it improves safety, 00:08:40.129 --> 00:08:42.710 too. Definitely. AI safety is another benefit 00:08:42.710 --> 00:08:45.549 mentioned. Relying on retrieved sources, which 00:08:45.549 --> 00:08:47.909 you could potentially trace or cite, builds trust. 00:08:48.330 --> 00:08:51.330 And then there's flexibility. RG lets LLMs tackle 00:08:51.330 --> 00:08:53.590 really complex questions that need information 00:08:53.590 --> 00:08:56.549 for massive amounts of text, internal or external, 00:08:57.049 --> 00:08:58.950 far more than could ever fit in a standard prompt. 00:08:59.210 --> 00:09:01.230 This sounds incredibly powerful, like turning 00:09:01.230 --> 00:09:03.710 a generalist AI into a specialist on demand. 00:09:04.110 --> 00:09:07.039 But, uh... building this. It can't be trivial, 00:09:07.179 --> 00:09:09.340 right? The article went into the components and 00:09:09.340 --> 00:09:11.799 steps needed to actually implement RG. No, you're 00:09:11.799 --> 00:09:13.080 right. It's definitely an engineering effort. 00:09:13.080 --> 00:09:14.899 It requires putting several key pieces together. 00:09:15.139 --> 00:09:17.059 It really starts with data ingestion and preparation. 00:09:17.159 --> 00:09:19.399 You've got to get the data from wherever it lives, 00:09:19.580 --> 00:09:22.639 APIs, files, databases. All the messy enterprise 00:09:22.639 --> 00:09:24.879 data sources. Exactly. And that often means a 00:09:24.879 --> 00:09:27.620 lot of work cleaning it, transforming it, maybe 00:09:27.620 --> 00:09:30.039 structuring it, especially company data. So it's 00:09:30.039 --> 00:09:33.240 actually usable by the RG system. Robust data 00:09:33.240 --> 00:09:35.159 pipelines are pretty key here. And you mentioned 00:09:35.159 --> 00:09:38.100 earlier that LLMs can only handle so much text 00:09:38.100 --> 00:09:41.580 at once. The context window limit. Right. Which 00:09:41.580 --> 00:09:45.139 leads directly to the next step. Chunking. You 00:09:45.139 --> 00:09:47.460 have to break down those potentially long documents 00:09:47.460 --> 00:09:50.919 into smaller, manageable pieces. Chunks. How 00:09:50.919 --> 00:09:53.399 do you decide how big the chunks should be? Well, 00:09:53.419 --> 00:09:55.299 the source discusses different ways. You could 00:09:55.299 --> 00:09:58.059 do fixed -length chunks, or maybe use NLP libraries 00:09:58.059 --> 00:10:00.799 like NLTK or Spacey, the article mentions, to 00:10:00.799 --> 00:10:03.019 be smarter about it. Like breaking its sentence 00:10:03.019 --> 00:10:05.929 or paragraph boundaries. What works best depends 00:10:05.929 --> 00:10:08.529 on the text itself, the kinds of questions people 00:10:08.529 --> 00:10:11.250 will ask, and definitely the context window size 00:10:11.250 --> 00:10:14.090 of the LLM you plan to use. Oh, and using some 00:10:14.090 --> 00:10:16.190 overlap between chunks is often a good idea, 00:10:16.309 --> 00:10:18.090 the source notes, to make sure you don't lose 00:10:18.090 --> 00:10:20.029 important context right at the chunk boundary. 00:10:20.169 --> 00:10:22.990 Okay, break it into overlapping pieces. Then 00:10:22.990 --> 00:10:25.190 you need a fast way to find the right piece when 00:10:25.190 --> 00:10:27.529 a question comes in. How's that done? That's 00:10:27.529 --> 00:10:29.789 where embeddings come in. Each of those text 00:10:29.789 --> 00:10:32.029 chunks gets converted into a numerical vector 00:10:32.029 --> 00:10:34.559 and embedding. You use a special model for this, 00:10:34.659 --> 00:10:36.759 an embedding model. The article mentions ones 00:10:36.759 --> 00:10:40.120 like text embedding at a 002 or 003. And these 00:10:40.120 --> 00:10:42.360 embeddings capture the meaning. Exactly. They 00:10:42.360 --> 00:10:45.100 capture the semantic meaning. So chunks that 00:10:45.100 --> 00:10:47.220 talk about similar things will have vectors that 00:10:47.220 --> 00:10:50.139 are mathematically close in this high dimensional 00:10:50.139 --> 00:10:53.860 space. Fascinating. And where do you store all 00:10:53.860 --> 00:10:55.980 these chunks and their vector embeddings? They 00:10:55.980 --> 00:10:58.820 go into specialized databases called vector databases. 00:10:59.460 --> 00:11:01.980 The article gives examples like Redis, Azure 00:11:01.980 --> 00:11:06.100 AI Search, Pinecone, Weaviate. These databases 00:11:06.100 --> 00:11:08.879 are built specifically for handling vectors efficiently. 00:11:09.139 --> 00:11:11.840 They create a vector index. An index for vectors. 00:11:11.980 --> 00:11:14.259 Yeah, which lets you do incredibly fast similarity 00:11:14.259 --> 00:11:16.679 searches, finding vectors that are close to a 00:11:16.679 --> 00:11:19.259 target vector, like our query vector. The source 00:11:19.259 --> 00:11:21.419 also points out that Some platforms like Azure 00:11:21.419 --> 00:11:23.820 AI Search can do hybrid search, combining that 00:11:23.820 --> 00:11:26.159 vector similarity with traditional keyword search 00:11:26.159 --> 00:11:30.899 can be really effective. OK, so data ingested, 00:11:31.259 --> 00:11:34.320 cleaned, chunked, embedded, and indexed in a 00:11:34.320 --> 00:11:37.659 vector db. Now I ask my question, walk me through 00:11:37.659 --> 00:11:40.460 the search. Right, so your question, your query, 00:11:40.799 --> 00:11:44.019 also gets converted into a vector using the exact 00:11:44.019 --> 00:11:46.259 same embedding model that was used for the chunks. 00:11:46.639 --> 00:11:49.700 Consistency is key. Absolutely. Then, this query 00:11:49.700 --> 00:11:51.720 vector is used to search against the vector index 00:11:51.720 --> 00:11:54.500 in the database. The database very quickly finds 00:11:54.500 --> 00:11:57.440 and returns the chunks whose stored vectors are 00:11:57.440 --> 00:11:59.980 most similar, most close, to your query vector. 00:12:00.399 --> 00:12:02.299 Those are the most semantically relevant bits 00:12:02.299 --> 00:12:04.759 of info from your entire knowledge race for that 00:12:04.759 --> 00:12:06.600 specific question. And those are the chunks that 00:12:06.600 --> 00:12:09.360 get sent to the LLM? Almost. There's one more 00:12:09.360 --> 00:12:12.539 critical step before the LLM sees anything. Prompt 00:12:12.539 --> 00:12:15.179 formulation. You have to dynamically build the 00:12:15.179 --> 00:12:17.120 prompt that goes to the LLM. Build the prompt? 00:12:17.259 --> 00:12:19.620 Yeah, you take the user's original query and 00:12:19.620 --> 00:12:21.720 you insert the relevant chunks you just retrieved. 00:12:21.879 --> 00:12:23.960 This whole package query plus context is put 00:12:23.960 --> 00:12:26.620 together, often using a specific API format like 00:12:26.620 --> 00:12:29.919 the Chat Completion API. And importantly, you 00:12:29.919 --> 00:12:32.399 include instructions for the LLM. Like use only 00:12:32.399 --> 00:12:35.049 this information. Exactly that kind of instruction. 00:12:35.389 --> 00:12:37.669 Answer the question based only on the provided 00:12:37.669 --> 00:12:39.929 context. And there's a technical constraint here 00:12:39.929 --> 00:12:41.830 the article mentions, managing the token budget. 00:12:42.289 --> 00:12:43.929 You have to make sure that query plus all the 00:12:43.929 --> 00:12:46.009 retrieved chunks don't exceed the LLM's maximum 00:12:46.009 --> 00:12:48.809 input size. It's context window limit. Right. 00:12:48.929 --> 00:12:51.870 Can't send it too much stuff. And then, finally... 00:12:51.870 --> 00:12:54.490 Then, that carefully constructed augmented prompt 00:12:54.490 --> 00:12:58.070 is passed to the LLM API. The LLM takes it all 00:12:58.070 --> 00:13:00.169 in and generates the final answer in natural 00:13:00.169 --> 00:13:02.330 language, using the query for direction and, 00:13:02.370 --> 00:13:05.049 crucially, relying on that specific retrieved 00:13:05.049 --> 00:13:08.210 context as its factual foundation. Wow. Okay, 00:13:08.210 --> 00:13:10.450 so there's quite a pipeline there. Ingestion, 00:13:10.649 --> 00:13:13.169 chunking, embedding, indexing, retrieval, prompt 00:13:13.169 --> 00:13:15.730 construction, all before the LLM even generates 00:13:15.730 --> 00:13:18.350 a word. But just getting it working technically 00:13:18.350 --> 00:13:20.169 isn't the end of the story for a business, is 00:13:20.169 --> 00:13:22.509 it? The article mentioned deployment considerations 00:13:22.509 --> 00:13:25.110 for actually using this in an enterprise. Absolutely. 00:13:25.289 --> 00:13:28.669 Getting RAG into production reliably involves 00:13:28.669 --> 00:13:31.490 thinking about several other factors. Scalability 00:13:31.490 --> 00:13:34.129 is huge. The system needs to handle potentially 00:13:34.129 --> 00:13:37.549 lots of users and the data keeps growing. Vector 00:13:37.549 --> 00:13:39.990 databases help here. They're often designed to 00:13:39.990 --> 00:13:42.289 scale. Performance must be key too. People won't 00:13:42.289 --> 00:13:44.929 wait forever for an answer. Definitely. Performance 00:13:44.929 --> 00:13:47.450 and latency. You need to track things like token 00:13:47.450 --> 00:13:49.830 usage, how long it takes to get a response back, 00:13:50.129 --> 00:13:53.090 which leads to observability. You need good monitoring 00:13:53.090 --> 00:13:55.940 and logging. The article mentions tools like 00:13:55.940 --> 00:13:58.860 MLflow or Prometheus, ways to see what's happening, 00:13:59.340 --> 00:14:01.399 track performance, and debug when things go wrong. 00:14:01.539 --> 00:14:03.240 And it has to play nice with existing systems, 00:14:03.299 --> 00:14:05.899 I imagine. Exactly. Integration. Yeah. How does 00:14:05.899 --> 00:14:08.100 this RRag system fit into the company's existing 00:14:08.100 --> 00:14:10.740 workflows and applications? And finally, maybe 00:14:10.740 --> 00:14:13.779 most importantly for enterprise, security and 00:14:13.779 --> 00:14:16.090 governance. Well, you need robust security. You 00:14:16.090 --> 00:14:18.830 need to manage data access, making sure users 00:14:18.830 --> 00:14:21.169 only retrieve information they're actually allowed 00:14:21.169 --> 00:14:24.110 to see based on their permissions. And of course, 00:14:24.250 --> 00:14:26.769 ensuring everything complies with relevant industry 00:14:26.769 --> 00:14:30.210 regulations or data privacy laws. So when you 00:14:30.210 --> 00:14:33.389 step back, the article really paints RAG as this 00:14:33.389 --> 00:14:35.970 foundational technique. It fundamentally changes 00:14:35.970 --> 00:14:38.629 how we can use these powerful LLMs. Yeah, it 00:14:38.629 --> 00:14:41.049 seems like it transforms them from being these 00:14:41.049 --> 00:14:43.899 like... general knowledge boxes with static info 00:14:43.899 --> 00:14:46.980 into dynamic tools that can actually interact 00:14:46.980 --> 00:14:50.259 with specific current even private company data. 00:14:50.460 --> 00:14:52.600 That's exactly it. It cleverly combines finding 00:14:52.600 --> 00:14:54.720 the right information efficiently, the retrieval 00:14:54.720 --> 00:14:57.320 part, with the amazing text generation abilities 00:14:57.320 --> 00:15:00.840 of LLMs. And by doing that, ARRAG really empowers 00:15:00.840 --> 00:15:03.519 organizations to build AI applications that are 00:15:03.519 --> 00:15:05.820 much more accurate, much more relevant to their 00:15:05.820 --> 00:15:08.559 specific needs, and safer because they're grounded 00:15:08.559 --> 00:15:11.100 in verifiable information. They can be tailored 00:15:11.100 --> 00:15:13.570 to a company's unique knowledge landscape. That 00:15:13.570 --> 00:15:16.210 really was a deep dive into RRag, breaking down 00:15:16.210 --> 00:15:18.389 the what, the why, and the how based on that 00:15:18.389 --> 00:15:20.830 source material. Really helpful. Thank you. My 00:15:20.830 --> 00:15:22.909 pleasure. And maybe it's something for you, the 00:15:22.909 --> 00:15:24.990 listener, to think about. Considering how important 00:15:24.990 --> 00:15:28.350 it is to ground AI in specific verifiable knowledge, 00:15:29.029 --> 00:15:31.909 how might this idea of retrieval augmented generation 00:15:31.909 --> 00:15:34.669 start to reshape the way you think about finding, 00:15:34.830 --> 00:15:36.970 trusting, and using information in your own work 00:15:36.970 --> 00:15:39.129 or your studies, or maybe even just in everyday 00:15:39.129 --> 00:15:39.450 life?