Vector Search
topic on 4 shows · 9 statements across 6 episodes
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No Priors 
the MAD Podcast 
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9 statements about Vector Search, every show
Eifrem: Good Enough Vector Features in Existing Databases Suffice
“Between everyone else adding it as a feature there, like the good enough ends up being good enough for most situations.”
Eifrem: GraphRAG combines vector search with graph traversal
“It's not like graph or vector search. It's like vector search in combination with traversing the graph. That's the typical kind of pattern that we see.”
Jain: Vector Search Alone Cannot Solve Enterprise Search
“There's a lot of focus on embeddings and vector search over the last few years, but that's actually only one part of, ah, building a good search system. Because if you think about an enterprise, ah, imagine a company that has been around for a few decades. You…”
Huber: Fuzzy search is most useful when users don't know the dataset
“Fuzzy search is really useful when people like are not, you know, experts in their own data, right? Is that if you're Google Drive, you know how to search for stuff pretty well, right? But like your users don't know how to search for the stuff that you've said…”
Goyal: Vector Search Hard Part Is Application Permissions, Not Search
“The problem is that the challenge in deploying vector search has very little to do with vector search itself, and much more to do with the data adjacent to vector search. So, for example, if you are at Figma, the Vector search is not actually the hard problem.…”
Goyal: Vector Search Is Rarely a Storage or Performance Bottleneck
“In almost all cases, vector search is not a storage or performance bottleneck. And in almost all cases, the vector search involves exactly one query, which is, you know, nearest neighbors.”
Van Luijt: Pure vector search alone is often not enough
“Vector search alone is often not enough, right?”
Van Luijt: Vector models perform poorly on specific alphanumeric IDs
“Matching on A, B, C, one, two, three is horrible. That's like, the model is super bad at that. Why? Because it was never trained on your on your knowledge base.”
Liu: Post-hype reality check will expose the limits of AI search techniques
“And I don't know, I think in the next year or two, maybe as like the, as we get past like the peak AI hype, we'll start to see the gap emerge or become more obvious to more people about like how, how, how many of like the newfangled techniques actually work in…”