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Logan Kilpatrick: evaluation

8 Feb 2024 Lenny's Podcast Inside OpenAI | Logan Kilpatrick (head of developer relations)

“I think historically, people really were only using embeddings for... It only worked really well for English, and I think now you can use it across so many new languages because it's just so much more performant across those languages and it's five times cheaper as well, which is wonderful.”

— Logan Kilpatrick

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Everything needed to verify it.

Speaker
Logan Kilpatrick
Attribution
Verified speaker
Claim type
evaluation
Recorded
8 Feb 2024
Publisher
Lenny's Podcast

Transcript context

…Lennybot.com. Check it out. Yeah, lennybot.com. And my assumption was that lennybot.com is actually powered by embedding. So you take all of the corpus of knowledge. You take all the recordings, your blog post. You embed them, and then when people ask questions, you can actually go in and see the similarity between the question and the corpus of knowledge and then provide an answer to somebody's question and reference an empirical fact, something that's true from your knowledge base. And this is super useful and people are doing a ton of this. It's like trying to ground these models in reality, in what they know to be true. We know all the things from your podcast to be at least something that you've said before and to be true in that sense, and we can bring them into the answer that the model is actually generating in response to a question. So that'll be super cool. And these new V3 embeddings models, again, state-of-the-art performance, the cool thing is actually the non-English performance has increased super significantly. I think historically, people really were only using embeddings for... It only worked really well for English, and I think now you can use it across so many new languages because it's just so much more performant across those languages and it's five times cheaper as well, which is wonderful. There's no better feeling than making things cheaper for people. I love it. I think now it's like you can embed, I'm pretty sure, it was like 62,000 pages of text for $1, which is very, very cheap. So lots of really cool things that you can do with embeddings and exciting to see people invent more stuff. What a deal. Final question before we get to a very exciting lightning round. Say you're a product manager at a big company, or even a founder, what do you think are the biggest opportunities for them to leverage the tech that you guys are building, GPT-4, all the other APIs? How should people be thinking about, "Here's how we should really think about leveraging this power in our existing product," or new product, whichever direction you want to go.…

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