Graph RAG Project Problem Statement

Key Takeaways

Welcome back. In module two, we will be explaining the problem statement. In module one, we covered all the theory behind Graphra. Now, we shift gears completely. This entire module is hands-on. And to make it concrete, we are going to follow a character throughout. Someone whose problem will feel very familiar. Meet Arin. He's 26 working as an AI professional and follows six podcast religiously. Lex Fredman, My First Million, Hubberman Lab, and few others. Every week there are new episodes dropping. Each one is two or three hours long. He generally wants to stay on the top of what guests are saying about AI, startups, and technology. But there is simply not enough time to listen to everything. Six podcast, 20 plus hours of content every week. Zero time to listen to all of it. He's not lazy. He's overwhelmed. And he has an idea. Here's Arin actual frustration. He doesn't want to sit and listen. He wants answers. Specific answers like what does Sam Altman think about AGI timelines or which guest disagree on whether AI will take jobs or what have guest said about open-source AI? These are real question he has. But none of these answers live in one episode. Sam Alman said something in episode 12. Yan Lee Quun said something different in episode 47. The full picture is scattered across dozens of hours of audio. There is no way to get to it quickly. Now you might think, why not just search the transcript? The problem runs deeper than that. First, transcripts are long and messy. This is spoken language, filler words, incomplete sentences, no clean structure, not easy to query. Second, answers span episodes. The question is not what did one person say in one episode. It is what did multiple people say across multiple episodes and how do their views compare. Third, opinion conflict and that is actually the interesting part. Attribution and the context matter as much as the answer itself. Knowing that Sam Alman said X means nothing if you don't know when he said it and in the response to what. This is not a search problem. This is a knowledge retrieval problem. So here's Arin's idea. He already has the transcripts. He just need the system he can query. The formal problem statement is build a system where I can feed podcast transcripts and ask any question across all episodes and get accurate sourced answer. And his plan to get there is two steps. Step one, build it with rag. See how far it gets and where it breaks. Step two, rebuild with graph rag and fix what broke. This is exactly the path we will follow in this module. We start with the simpler solution, expose its limits on the specific problems and then build the right solution from scratch. So that is the problem statement. In the next video, Arian builds his first solution using rack. The code is clean. The output looked decent at first glance, but something is missing. Let's find out what. See you there.