George Sivulka

So he's like, well, let's just go add some fuel to the fire. How much did he invest in? He, I think, invested like an additional $2 or $2.5 million at the time. So where did the $130 come from? Well, that was years later. This was years later? So this is all in 2020. We end up building a product studio, which is the first to productionize RAG, Retrieval Augmented Generation, also in 2020.

So he's like, well, let's just go add some fuel to the fire. How much did he invest in? He, I think, invested like an additional $2 or $2.5 million at the time. So where did the $130 come from? Well, that was years later. This was years later? So this is all in 2020. We end up building a product studio, which is the first to productionize RAG, Retrieval Augmented Generation, also in 2020.

We build the first semantic search engine. We actually go out and start to- Can you just help us understand what is RAG? RAG is an acronym that stands for Retrieval Augmented Generation. If you look at large language models today, they're really good at maybe thinking if you give it the right context, but they hardly ever have the right context.

We build the first semantic search engine. We actually go out and start to- Can you just help us understand what is RAG? RAG is an acronym that stands for Retrieval Augmented Generation. If you look at large language models today, they're really good at maybe thinking if you give it the right context, but they hardly ever have the right context.

We build the first semantic search engine. We actually go out and start to- Can you just help us understand what is RAG? RAG is an acronym that stands for Retrieval Augmented Generation. If you look at large language models today, they're really good at maybe thinking if you give it the right context, but they hardly ever have the right context.

And so RAG was the first real attempt to give them the data to answer questions correctly. Okay. And so we are building on RAG to start. We were one of the first people to productionize the idea of putting a search engine behind an LLM. So you'd ask a question and then instead of it just replying from its memory, it would actually go and do a search and then reply with that context.

And so RAG was the first real attempt to give them the data to answer questions correctly. Okay. And so we are building on RAG to start. We were one of the first people to productionize the idea of putting a search engine behind an LLM. So you'd ask a question and then instead of it just replying from its memory, it would actually go and do a search and then reply with that context.

And so RAG was the first real attempt to give them the data to answer questions correctly. Okay. And so we are building on RAG to start. We were one of the first people to productionize the idea of putting a search engine behind an LLM. So you'd ask a question and then instead of it just replying from its memory, it would actually go and do a search and then reply with that context.

So in an enterprise where you have a lot of offline data, we were really the first people to hook up that offline data to large language models to answer that question.

So in an enterprise where you have a lot of offline data, we were really the first people to hook up that offline data to large language models to answer that question.

So in an enterprise where you have a lot of offline data, we were really the first people to hook up that offline data to large language models to answer that question.

That's a bit of a plot twist over here. I actually don't think RAG works at all. It's one of the most used AI architectures in the world, pioneered at Hebbia in a very meaningful way. I think every enterprise is experimenting with it, but it has a lot of different failures where a lot of the time, the questions that people ask these systems aren't ever explicitly in the data.

That's a bit of a plot twist over here. I actually don't think RAG works at all. It's one of the most used AI architectures in the world, pioneered at Hebbia in a very meaningful way. I think every enterprise is experimenting with it, but it has a lot of different failures where a lot of the time, the questions that people ask these systems aren't ever explicitly in the data.

That's a bit of a plot twist over here. I actually don't think RAG works at all. It's one of the most used AI architectures in the world, pioneered at Hebbia in a very meaningful way. I think every enterprise is experimenting with it, but it has a lot of different failures where a lot of the time, the questions that people ask these systems aren't ever explicitly in the data.

They're never explicitly stated. They're actually about the data. So for example, if you're asking an AI system, is this company a good investment? which is actually a very common thing that people ask Hebbia over marketing materials. Maybe it'll say in a pitch deck, yeah, this company is a great investment as something that the CEO says or like a recording, et cetera.

They're never explicitly stated. They're actually about the data. So for example, if you're asking an AI system, is this company a good investment? which is actually a very common thing that people ask Hebbia over marketing materials. Maybe it'll say in a pitch deck, yeah, this company is a great investment as something that the CEO says or like a recording, et cetera.

They're never explicitly stated. They're actually about the data. So for example, if you're asking an AI system, is this company a good investment? which is actually a very common thing that people ask Hebbia over marketing materials. Maybe it'll say in a pitch deck, yeah, this company is a great investment as something that the CEO says or like a recording, et cetera.

But what you actually want from that system isn't a search in the data, it's an answer about the data. Hey, what's the customer concentration? What's the strength of the management team? What are X, Y, or Z criteria that are fundamental to our specific investing process? And that's a process. That's not ever explicitly stated. Actually, the marketing materials are often like a load of crap.

But what you actually want from that system isn't a search in the data, it's an answer about the data. Hey, what's the customer concentration? What's the strength of the management team? What are X, Y, or Z criteria that are fundamental to our specific investing process? And that's a process. That's not ever explicitly stated. Actually, the marketing materials are often like a load of crap.

But what you actually want from that system isn't a search in the data, it's an answer about the data. Hey, what's the customer concentration? What's the strength of the management team? What are X, Y, or Z criteria that are fundamental to our specific investing process? And that's a process. That's not ever explicitly stated. Actually, the marketing materials are often like a load of crap.