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Simon Hørup Eskildsen: evaluation

12 Mar 2026 Latent Space Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Hørup Eskildsen of Turbopuffer

“Like Notion does a ridiculous amount of queries in every round trip just because they can’t. And I’m also now, when I use the cursor agent, I also see them doing more concurrency than I’ve ever seen before.”

— Simon Hørup Eskildsen

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Speaker
Simon Hørup Eskildsen
Attribution
Verified speaker
Claim type
evaluation
Recorded
12 Mar 2026
Publisher
Latent Space

Transcript context

…Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it’s searching it again later. Yeah. What are maybe some of the new types of workloads or like changes you’ve had to make to your architecture for it? I think you’re right. When I think of rag, I think of, Hey, there’s an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing. Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we’re just the tool call, right? And that’s increasingly what we see our customers doing. Um, what we’re seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can’t. And I’m also now, when I use the cursor agent, I also see them doing more concurrency than I’ve ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That’s also what the agents are doing. So that’s new. It means just an enormous amount of queries all at once to the dataset while it’s warm in as few turns as possible. Can I clarify one thing on that?…

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