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Right, ready to dive into some seriously cool AI research.

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Absolutely, always up for that.

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Awesome.

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Today we're looking at main rag,

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which stands for multi-agent filtering

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or retrieval augmented generation.

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Catchy name, right.

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It is, but what's even cooler is what it does.

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It's all about making large language models.

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You know those AI systems that can like understand

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and generate text, even smarter and more reliable.

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Yeah, those LLMs can be pretty amazing,

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but one of the things that this research really dives into

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is how to deal with the fact

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that sometimes they make things up.

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Oh yeah, or they give you info that's totally outdated.

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Exactly, and that's because they're working

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with limited data, so main rag is trying to fix that.

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You know how annoying it is to ask your AI assistant something

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and get an answer that just feels off?

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Oh yeah, totally.

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Like wait, that doesn't sound right.

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Yeah, exactly, like imagine asking for directions

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and your AI sends you down a road

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that's been closed for years.

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Oh, that's the worst.

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Right, and it's because its knowledge is stuck in the past.

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So to make AI more accurate, researchers have been using

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something called retrieval augmented generation,

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or AG for short.

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Okay, so AG gives these LLMs access

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to more up-to-date info from like external sources.

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Yeah, think of it like giving the AI access

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to a whole library of information.

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But I'm guessing it's not as simple

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as just giving it access to everything, right?

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You're right, even with AG you can still end up

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with a lot of irrelevant or unreliable information mixed in.

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Ah, so it's like trying to write a report

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with a bunch of random pages

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scattered throughout your research.

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Exactly, it can make it really hard to find what you need,

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and that's where main rag comes in.

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So main rag is like a quality control system,

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making sure that AI is using the best possible information.

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Exactly, it uses a team of three AI agents

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to filter and rank the documents it pulls in

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from outside sources.

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Oh wow, three AI agents, sounds like a team effort.

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It is, think of them like specialized researchers.

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Okay, I'm intrigued, tell me more about these agents,

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how do they work?

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All right, so let's start with Agent One,

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this one is called the Predictor.

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Okay, the Predictor, what does it do?

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Well, it takes the question that you've asked,

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and then tries to come up with possible answers

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based on each document that it finds.

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So it's like each document is being interviewed

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for the job of providing the best answer.

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Yeah, that's a great way to think about it.

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And then once the Predictor has generated

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these potential answers from each document,

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Agent Two steps in, this one's called the Judge.

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Okay, the Judge, this is where it gets interesting.

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Great, so the Judge analyzes each document,

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it also looks at those potential answers

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that the Predictor came up with.

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I see.

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And its job is to figure out

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if the document actually supports the answer

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and how relevant it is to the question.

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So it's like the Judge is checking the facts,

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making sure everything lines up.

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Right, and then based on this whole analysis,

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the Judge assigns a score to each document.

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This score is super important

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because it helps determine which documents

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are trustworthy and which ones aren't.

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Ah, so it's like a ranking system.

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The most reliable sources get the highest scores.

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Exactly, and this is where the Adaptive Judge bar comes in.

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Okay, Adaptive Judge bar, tell me more.

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So think of this bar as like setting a standard

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for how trustworthy a document needs to be.

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Okay, I'm following.

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So anything below the bar gets filtered out.

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So it's like a filter only letting in the most relevant

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and reliable information.

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Yes, but here's the really clever part.

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This bar isn't fixed.

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It actually adapts based on the scores

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that the Judge is giving out.

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Yeah, so if the Judge is seeing

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that a lot of the documents are scoring high,

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meaning they're really relevant to the question,

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it raises the bar.

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Makes sense.

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So only the best of the best get through.

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Exactly, but you know,

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if most of the documents aren't great,

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well, the bar gets lowered.

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Okay, so it's still filtering out

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the worst of the worst just being a little more lenient.

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Yeah, exactly.

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It's not gonna let in total garbage,

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but it's also not gonna be super strict

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if there's just not much good info out there.

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So this Adaptive Judge bar,

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it's kind of the secret sauce here, right?

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Yeah, I think so.

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You know, it means that main rag

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is constantly learning and adapting.

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It's getting smarter about what information

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it can actually trust.

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Exactly, and you know, this approach is really cool

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because it doesn't need tons of data

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or training to learn how to do this.

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So it's like a plug and play solution to make AI smarter.

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Pretty much.

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That's really impressive.

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But I'm curious,

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if we're letting in even slightly irrelevant documents,

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wouldn't that make the AI more likely to mess up?

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You'd think so, right?

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You'd think a little noise would throw things off.

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Yeah.

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But even when the judge isn't 100% certain

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about a document, it still gives higher scores

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to ones that have at least some useful information.

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Oh, interesting.

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So even if a document isn't a perfect match,

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it might still have some valuable insights.

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Yeah, exactly.

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It's all about extracting as much knowledge as possible.

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I see.

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So main rag is able to like squeeze every drop

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of relevant information out of the sources it can find.

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It's not just looking for a perfect match.

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Right, it's like looking at the bigger picture.

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Okay, that makes a lot of sense.

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So now we've got these documents,

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they've been filtered and ranked, what happens next?

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Right, so that brings us to Agent Three.

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This is the final agent in this whole system,

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and it's called the final predictor.

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The final predictor, okay.

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And so this agent, it takes all those filtered

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and ranked documents from the judge,

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and then uses that information to give the final answer.

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The hopefully more accurate answer,

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thanks to all that filtering and ranking.

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Exactly.

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Okay, I'm really liking this team effort approach,

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but does it actually work?

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I mean, what kind of results did they see?

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Oh, it definitely works.

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They tested main rag on four different

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question answering tasks.

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Okay, cool, four different tasks.

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Like one of them was open domain question answering.

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So that's where the AI has to like search

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through a massive database to find the answer?

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Yeah, exactly.

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And they even tested it

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on a long form question answering task.

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So those are the questions where the answers

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are like more complex,

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and they need a deeper understanding of the topic.

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Right.

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Okay, so tell me, how did main rag do?

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It consistently outperformed other systems.

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Seriously.

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Yeah.

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Sometimes by as much as 11% in accuracy.

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Wow, that's a big difference.

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It is, and it even beats some systems

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that had a lot more training.

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So it's not just more accurate, it's also more efficient.

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Yeah, and it did all of this

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while using fewer computational resources.

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That's amazing.

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So this is all very impressive,

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but what does it actually mean for the average person?

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How could this change the way we interact with AI?

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Well, imagine if your search engine

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could always give you the most relevant

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and up to date results.

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Oh yeah, no matter how obscure or weird your search is.

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Exactly, or think about those customer service chat bots.

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Ugh, those can be so frustrating.

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I know, but imagine if they could actually understand

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your questions and give you accurate answers.

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And not have to transfer me to a human

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like five minutes into the conversation.

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Right, this is the kind of future

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that main rag could help create.

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Okay, yeah, that would be a game changer for sure.

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But what about other areas of AI?

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I mean, beyond just question answering,

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could this technology be applied to other tasks as well?

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Absolutely, it could totally revolutionize

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a whole bunch of AI applications.

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Well, think about things like summarizing complex documents

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or translating languages.

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But even more, think about generating creative content.

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Content that's actually original and informative.

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Exactly, the possibilities are huge.

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Okay, now I'm really excited.

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But before we get too carried away

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dreaming about the future,

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can you maybe give us a bit more detail

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about how main rag actually works in practice?

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Sure.

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Can you walk us through a specific example?

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Of course.

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So let's take a look at how main rag handles real world questions.

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Okay.

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I've got a few case studies here

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that really show how it filters and ranks information

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to get to the right answer.

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All right, let's hear them.

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I'm ready to see main rag in action.

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Okay, hit me with those case studies.

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I'm dying to see main rag in action.

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All right, let's start with a question

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from this data set called PopQA.

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It's all about those like random trivia questions.

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You know the ones that make you go,

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huh, I wonder about that.

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Yeah, yeah, I know exactly what you mean.

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So the question is in what city was Monsumeranda born?

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Ooh, okay, you got me.

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Yeah.

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I have no idea.

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Me neither.

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But don't worry, main rag was up for the challenge.

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Okay, good.

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So it pulls up all these documents about Monsumeranda.

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Who is Monsumeranda?

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Turns out he's a Spanish pole valter.

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Okay.

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And some of these documents mention his birth year

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or they talk about his childhood,

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but only one actually says he was born in Santers, Spain.

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Wow, so it's like finding a needle in a haystack.

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Exactly.

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But here's where that adaptive judge bar comes in.

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Since most of the documents didn't actually have the answer,

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the bar was set a little bit lower.

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Right, makes sense.

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But Agent Two, the judge, was still able to pick out

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that one document that directly answered the question.

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And give it a much higher score so it stands out.

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Right.

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So even with all that irrelevant info,

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main rag could still find what it needed.

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Exactly.

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And that's how Agent Three, the final predictor,

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confidently gave the right answer, Santers.

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Very cool.

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But what happens when there are multiple documents

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that have the right answer?

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Does main rag just pick one at random?

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That's a great question.

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Let's look at another example,

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this time from the Trivia QA data set.

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The question is, where is the area of 127,000 square kilometers

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in central South Australia,

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where the public is not allowed

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under Australian Defense Force rules?

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Oh, wow, that's super specific.

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Right, you need some pretty new knowledge

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to answer that one.

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I bet.

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So for this one, main rag found a bunch of documents

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about these restricted areas in Australia.

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Okay.

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And a lot of them mentioned this place called

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the Wumer Prohibited Area.

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Which fits the description.

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Exactly.

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So how does main rag choose which document

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to actually use when there are multiple ones

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that have the right answer?

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Well, remember how the judge assigns those scores

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based on how certain it is about a document?

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Yeah.

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Well, that's where this gets interesting,

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because even though multiple documents might talk

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about the Wumer Prohibited Area,

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some of them might have given stronger evidence

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or more detail.

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I see.

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So it's not just about finding the answer,

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it's about finding the best source for that answer.

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Precisely.

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So by ranking the documents

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and putting the most relevant one first,

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it makes sure that the final predictor

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has the best possible information.

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Like main rag is curating the information,

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almost like a librarian would do for a research project.

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Yeah, that's a great analogy.

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It's not just retrieving documents,

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it's like carefully selecting and organizing them

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to make sure the AI gets the best possible results.

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This has been a super fascinating deep dive.

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I feel like I have a much better understanding

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of how we can make AI smarter.

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Any final thoughts before we wrap things up?

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Yeah, I think the big takeaway here is that the future of AI

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isn't just about creating these bigger

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and more complex models.

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It's also about finding smarter ways

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to manage and process information.

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Main rag is a really great example of that,

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showing that we can use AI to help solve

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some of the problems that AI creates.

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It's like AI becoming self-aware and improving itself,

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which is both exciting and a little bit mind-blowing

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to think about.

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I know, right?

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00:11:11,880 --> 00:11:12,880
It's really cool stuff.

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00:11:12,880 --> 00:11:14,520
Well, thanks for guiding us through this research.

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00:11:14,520 --> 00:11:16,640
It's been a really insightful deep dive.

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My pleasure.

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Always happy to talk about the latest in AI research.

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And to all our listeners, until our next deep dive,

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keep those brains curious.