Embedding Models
topic on 4 shows · 8 statements across 5 episodes
Latent Space 
Lenny's Podcast 
No Priors 
the MAD Podcast
8 statements about Embedding Models, every show
Mosseri: Recommender systems rely on illegible vectors, not semantic profiles
“I think people assume that there's a much more detailed semantic understanding of everybody's interests and preferences in the algorithm than there is. Most of what's really driven the progress in the world of recommenders over the last five, 10 years have bee…”
Bergum: Standalone embedding API startups face a difficult business model
“I think it's a difficult business model to be in, like, because you have to have an API based service and you have to do batching and you have to make up for the compute and then, you know, are people willing to pay for it? And I think maybe that's why Voyage …”
Fu: Embedding model quality barely matters for final RAG performance
“We had this experience over and over again where you could have any, an embedding model of any quality, so you could have a really, really bad embedding model, or you could have a really, really good one by, and by any measure of good, and for the final RAG ap…”
Ma: Agent Chaining Architectures Still Rely on Embedding Models
“On the first level bit I would say is that I think it's kind of orthogonal to embedding models and re-rankers to some degree, because even when you have agent chaining, right, you still probably use embedding models as part of the chain, right?”
Ma: Iterative Retrieval Will Diminish as Embedding Models Improve
“However, in the long run, my suspicion is that iterative retrieval will be useful, but it will be a bit less useful as the If the embedding models becomes more and more clever, right? So once the embedding models are more clever, then maybe one run or two runs…”
Ma: RAG Software Heuristics Will Vanish as Embedding Models Improve
“And my long term vision here is that some of the software engineering layers on top of the networks will be less and less needed when the networks are more and more clever.”
Ma: Latency Limits Embedding Models to 10 Billion Parameters
“Basically it's impossible to use more than ten billion parameters. For embedding models.”
Huber: Current SOTA LLMs Lack Reliability for Multi-Agent Workflows
“Now, of course, for those of you that have actually played with technology, I think it's questionable whether the current state of the art Language models, embedding models, et cetera, will give you the reliability you want from, ah, you know, agents working t…”