1 00:00:00,000 --> 00:00:01,320 Welcome back everyone. 2 00:00:01,320 --> 00:00:03,560 I'm Mukundan Sankar, your host. 3 00:00:03,560 --> 00:00:04,760 And if you're new here, 4 00:00:04,760 --> 00:00:06,700 let me tell you a bit about myself. 5 00:00:07,640 --> 00:00:10,040 I'm an experienced data professional 6 00:00:10,040 --> 00:00:13,100 and I have been in the industry for about seven years now. 7 00:00:13,100 --> 00:00:16,640 I'm an active researcher in data and AI. 8 00:00:16,640 --> 00:00:20,920 So everything data, including data analysis, data science, 9 00:00:20,920 --> 00:00:23,080 analytical engineering, machine learning, 10 00:00:23,080 --> 00:00:24,980 deep learning, and now AI. 11 00:00:24,980 --> 00:00:29,980 So the roles that I've held and the projects that I've done 12 00:00:31,100 --> 00:00:36,100 have helped me get this far and get these skills. 13 00:00:36,200 --> 00:00:38,780 So I've always been fascinated by how technology 14 00:00:38,780 --> 00:00:42,620 can help us navigate the complexities of everyday life. 15 00:00:42,620 --> 00:00:45,540 And today I'm really excited to share something with you 16 00:00:45,540 --> 00:00:48,780 that's gonna change how we deal with information overload. 17 00:00:48,780 --> 00:00:51,780 It's called retrieval augmented generation or RAG. 18 00:00:51,780 --> 00:00:56,780 So do you ever feel like you're drowning in information, 19 00:00:57,280 --> 00:00:59,120 like basically articles, reports, 20 00:00:59,120 --> 00:01:02,400 just endless amounts of data that make it hard to find 21 00:01:02,400 --> 00:01:04,120 anything that really matters? 22 00:01:05,000 --> 00:01:06,360 Well, you're not alone. 23 00:01:06,360 --> 00:01:09,400 We're living in an era where we constantly bombarded 24 00:01:09,400 --> 00:01:12,120 with more information than ever before. 25 00:01:12,120 --> 00:01:13,880 And it can get overwhelming, right? 26 00:01:14,880 --> 00:01:17,000 Here's the exciting part though. 27 00:01:17,000 --> 00:01:18,280 There's something that can help 28 00:01:18,280 --> 00:01:20,380 with this information overload. 29 00:01:20,380 --> 00:01:22,800 And it is called retrieval augmented generation 30 00:01:22,800 --> 00:01:23,760 or RAG for short. 31 00:01:25,040 --> 00:01:26,720 I know, I know it sounds super technical, 32 00:01:26,720 --> 00:01:29,080 but let me break it down for you so that 33 00:01:30,800 --> 00:01:32,800 when you're understanding why this is important 34 00:01:32,800 --> 00:01:35,980 and why this is seriously a game changer. 35 00:01:37,880 --> 00:01:40,840 So what is RAG exactly? 36 00:01:40,840 --> 00:01:44,040 Well, RAG is an AI model that combines the best 37 00:01:44,040 --> 00:01:47,600 of both worlds, that is retrieval and generation. 38 00:01:47,600 --> 00:01:49,520 So I'll just go with that again. 39 00:01:49,520 --> 00:01:52,880 The best of both worlds combines retrieval and generation. 40 00:01:53,880 --> 00:01:56,580 Let's start with the first part, the retriever. 41 00:01:59,080 --> 00:02:01,680 The retriever is basically doing that retrieval process. 42 00:02:01,680 --> 00:02:05,240 So imagine it like a super powered search engine. 43 00:02:05,240 --> 00:02:08,800 When you ask this RAG a question, 44 00:02:08,800 --> 00:02:13,800 the retriever goes out and scours vast amounts of data, 45 00:02:13,920 --> 00:02:16,880 documents, articles, reports, 46 00:02:16,880 --> 00:02:19,600 to find the most relevant information. 47 00:02:19,600 --> 00:02:21,320 But here's the catch. 48 00:02:21,320 --> 00:02:25,520 It's not just pulling up a list of links or keywords. 49 00:02:25,520 --> 00:02:28,040 No, it's smarter than that. 50 00:02:28,040 --> 00:02:30,360 The retriever is looking for deeper connections, 51 00:02:30,360 --> 00:02:32,700 patterns and context in the data, 52 00:02:32,700 --> 00:02:36,020 like a detective combing through clues to find the truth. 53 00:02:37,020 --> 00:02:39,080 And then once it gathers all the good stuff, 54 00:02:39,080 --> 00:02:42,200 the second part kicks in, the generator. 55 00:02:42,200 --> 00:02:44,840 Now the generator is like an amazing storyteller. 56 00:02:44,840 --> 00:02:47,520 It takes all that raw data that you just gave 57 00:02:47,520 --> 00:02:49,040 and all those facts and figures 58 00:02:49,040 --> 00:02:51,480 that the retriever basically dug up 59 00:02:51,480 --> 00:02:54,320 and it summarizes them for you in a way that's clear, 60 00:02:54,320 --> 00:02:56,000 concise and even a little fun. 61 00:02:56,880 --> 00:02:59,040 You're not reading through pages anymore 62 00:02:59,040 --> 00:03:01,880 and pages of technical jargon or dry statistics. 63 00:03:01,880 --> 00:03:03,400 The generator packages everything 64 00:03:03,400 --> 00:03:05,200 in a way that actually makes sense. 65 00:03:05,200 --> 00:03:06,360 So you might be thinking, 66 00:03:06,360 --> 00:03:08,760 how is this different from what we have now? 67 00:03:08,760 --> 00:03:11,440 Can't I just Google and basically read 68 00:03:11,440 --> 00:03:13,040 the results from Google? 69 00:03:13,040 --> 00:03:14,040 Well, here's the kicker. 70 00:03:14,040 --> 00:03:15,800 This is next level. 71 00:03:15,800 --> 00:03:17,720 You see, traditional AI models 72 00:03:17,720 --> 00:03:19,440 and even your trusty search engine, 73 00:03:19,440 --> 00:03:21,440 sometimes they miss the mark. 74 00:03:21,440 --> 00:03:24,200 They can pull in inaccurate or outdated information 75 00:03:24,200 --> 00:03:26,320 or worse, just make stuff up. 76 00:03:26,320 --> 00:03:29,280 That's what they call AI hallucination. 77 00:03:29,280 --> 00:03:31,040 Basically when the AI goes off script 78 00:03:31,040 --> 00:03:33,920 and creates information that isn't real. 79 00:03:33,920 --> 00:03:35,520 That's scary, right? 80 00:03:35,520 --> 00:03:37,600 But RAG, it's different. 81 00:03:37,600 --> 00:03:39,560 It's grounded in real data. 82 00:03:39,560 --> 00:03:41,720 The retriever makes sure of that. 83 00:03:41,720 --> 00:03:43,400 It doesn't just generate answers 84 00:03:43,400 --> 00:03:45,880 based on what the AI already knows, 85 00:03:45,880 --> 00:03:47,720 which could be limited outdated. 86 00:03:47,720 --> 00:03:50,360 Instead, it brings in fresh, 87 00:03:50,360 --> 00:03:53,320 up to date facts from reliable sources. 88 00:03:54,360 --> 00:03:58,800 This makes RAG way more accurate and trustworthy. 89 00:03:58,800 --> 00:04:02,120 And get this, this isn't just theoretical. 90 00:04:02,120 --> 00:04:05,240 RAG is being used for some seriously cool stuff. 91 00:04:05,240 --> 00:04:08,360 I for one, I have used it to convert text news articles 92 00:04:08,360 --> 00:04:12,680 to audio using LLMs, which is large language models 93 00:04:12,680 --> 00:04:14,360 with this RAG technology. 94 00:04:14,360 --> 00:04:18,640 And I will link the article in the show notes. 95 00:04:18,640 --> 00:04:22,440 So it's basically, just to give you a brief overview 96 00:04:22,440 --> 00:04:24,280 of what that article is. 97 00:04:24,280 --> 00:04:28,320 I've used an advanced large language model 98 00:04:29,240 --> 00:04:34,240 to convert news articles in text form into audio, 99 00:04:34,280 --> 00:04:38,560 basically using a Google text to speech 100 00:04:38,560 --> 00:04:42,720 and GTTS module to convert that. 101 00:04:42,720 --> 00:04:47,720 And it will give you the article in audio form. 102 00:04:47,800 --> 00:04:49,040 That's very interesting. 103 00:04:49,040 --> 00:04:51,360 And that was all possible thanks to RAG. 104 00:04:52,360 --> 00:04:56,920 So imagine, let's say you're imagining 105 00:04:56,920 --> 00:05:00,000 some deeper use case than what I wrote in my blog, right? 106 00:05:00,000 --> 00:05:03,280 A more personalized news podcast, for example. 107 00:05:03,280 --> 00:05:04,280 So what does that even mean? 108 00:05:04,280 --> 00:05:07,920 So basically, instead of just reading these long form 109 00:05:07,920 --> 00:05:11,680 text articles that you get, you get curated summaries, 110 00:05:11,680 --> 00:05:14,960 but in audio form as a podcast, for example. 111 00:05:15,880 --> 00:05:19,280 Right, like, I mean, I know we have like very amazing 112 00:05:19,280 --> 00:05:23,600 newsletters that we get through emails and that's great. 113 00:05:23,600 --> 00:05:25,240 I love that. 114 00:05:25,240 --> 00:05:28,320 But like this is maybe a podcast for you. 115 00:05:29,520 --> 00:05:31,040 That would be amazing, right? 116 00:05:31,040 --> 00:05:34,160 I mean, just think of all the amazing possibilities we have. 117 00:05:34,160 --> 00:05:37,480 So like the AI in this case would be scanning the news, 118 00:05:37,480 --> 00:05:40,400 picking up key points from the articles and boom, 119 00:05:41,280 --> 00:05:43,680 it turns it all into something you can listen to 120 00:05:43,680 --> 00:05:44,520 on your commute. 121 00:05:45,560 --> 00:05:47,960 It's not just about spitting facts either. 122 00:05:47,960 --> 00:05:50,680 The generator makes sure the final output is coherent 123 00:05:50,680 --> 00:05:53,680 and easy to understand, just like a human would explain 124 00:05:53,680 --> 00:05:56,680 it to you, but it doesn't stop with the news. 125 00:05:58,240 --> 00:06:01,240 Think about how this could apply to research papers, 126 00:06:01,240 --> 00:06:04,560 where it is like dense, complicated scientific reports 127 00:06:04,560 --> 00:06:06,960 that would take hours to read and understand. 128 00:06:08,080 --> 00:06:10,920 RAG would summarize the key breakthroughs for you 129 00:06:10,920 --> 00:06:11,760 in minutes. 130 00:06:12,720 --> 00:06:15,400 Or at work, imagine cutting down hours of slogging 131 00:06:15,400 --> 00:06:18,440 through long reports and instead having the AI 132 00:06:18,440 --> 00:06:21,040 highlight the most important points, making meetings 133 00:06:21,040 --> 00:06:24,440 and decision making so much faster and more efficient. 134 00:06:25,640 --> 00:06:28,480 What makes this even more exciting is that RAG 135 00:06:28,480 --> 00:06:29,600 has real potential. 136 00:06:29,600 --> 00:06:32,600 To personalize how we learn and process information. 137 00:06:33,640 --> 00:06:36,800 So instead of wading through mountains of irrelevant data, 138 00:06:36,800 --> 00:06:40,200 RAG narrows it down and presents what's actually useful. 139 00:06:40,200 --> 00:06:43,240 It's like having your own personal research assistant 140 00:06:43,240 --> 00:06:46,640 or librarian working at lightning speed, 141 00:06:46,640 --> 00:06:49,880 delivering exactly what you need when you need it. 142 00:06:49,880 --> 00:06:51,720 So yeah, RAG isn't just another buzzword, 143 00:06:51,720 --> 00:06:53,000 it's here to stay. 144 00:06:53,000 --> 00:06:55,560 It's going to change how we handle information, 145 00:06:55,560 --> 00:06:58,400 how we learn, how we work, and how we make decisions 146 00:06:58,400 --> 00:06:59,720 of the world. 147 00:07:00,680 --> 00:07:03,840 And like any powerful world, any powerful tool, 148 00:07:03,840 --> 00:07:05,720 it's up to us to use it wisely. 149 00:07:06,760 --> 00:07:08,520 Because at the end of the day, 150 00:07:08,520 --> 00:07:10,840 it's not just about managing information, 151 00:07:10,840 --> 00:07:14,520 it's about making it work for you, helping you focus 152 00:07:14,520 --> 00:07:15,800 on what really matters. 153 00:07:17,080 --> 00:07:19,640 So, and that's all for today. 154 00:07:19,640 --> 00:07:22,360 Just like to recap what I just said, 155 00:07:22,360 --> 00:07:24,200 retrieval augmented generation, 156 00:07:24,200 --> 00:07:26,360 or RAG is just truly game changer. 157 00:07:26,360 --> 00:07:30,600 You're going to, instead of having mountains 158 00:07:30,600 --> 00:07:34,600 of information, you know, you're looking through them 159 00:07:34,600 --> 00:07:37,200 personally, RAG would do that for you 160 00:07:37,200 --> 00:07:39,040 and give you more personalized insights 161 00:07:39,040 --> 00:07:40,840 that actually make sense. 162 00:07:40,840 --> 00:07:43,840 It's revolutionizing everything that I just mentioned before. 163 00:07:45,360 --> 00:07:47,520 As we move forward, it's important to remember 164 00:07:47,520 --> 00:07:49,320 that tools like RAG are only as good 165 00:07:49,320 --> 00:07:50,880 as how we choose to use them. 166 00:07:52,240 --> 00:07:55,200 And technology like this, as the power of the brain, 167 00:07:55,200 --> 00:07:57,520 technology like this as the power to shape our future. 168 00:07:57,520 --> 00:08:01,600 And you already know this, but we should use it wisely. 169 00:08:01,600 --> 00:08:02,440 Thanks for tuning in. 170 00:08:02,440 --> 00:08:04,560 And if this has sparked your curiosity, 171 00:08:04,560 --> 00:08:06,760 don't forget to subscribe because we'll be diving 172 00:08:06,760 --> 00:08:09,200 into even more exciting tech trends, 173 00:08:09,200 --> 00:08:12,160 data projects and innovations in the episodes to come. 174 00:08:12,160 --> 00:08:14,920 Until next time, keep exploring, keep learning 175 00:08:14,920 --> 00:08:16,720 and stay curious. 176 00:08:16,720 --> 00:08:25,720 And GeoAI is just getting started.