RAG: Retrieval Augmented Gathering
How are people building RAG in 2026? Is all search becoming agentic? If agents do everything, does retrieval quality still matter? Join me for a series of talks on agentic search, RAG, and retrieval. Come network with peers, explore modern RAG / retrieval implementations, and see how the agentic revolution is coming for search.
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Fri Sep 18·5:00 PM UTC
Search and AI Career Q&A
It's scary out there. Skills we thought would be timeless are being brought into question. We're all being asked to relearn our career as agentic coding continues to eat software development. Search roles, in particular, increasingly focus away from traditional search and instead focus on RAG and agents. Doug and Brian specializes in helping search teams develop and recruit talent. Bring your search career questions, we'll discuss them!
You'll learn from
Doug Turnbull (Maven)
Led teams at Shopify, Reddit, Wikipedia
Brian Pedersen
Principal, "The Search Bar" (AI Search Hiring Firm)
Tue Sep 22·5:00 PM UTC
Neural search at BM25 latency
SPLADE uses a neural model to expand your documents' keywords, so keyword search can match synonyms (and other semantically similar keywords). But it runs a model on every query, which adds about 50ms latency and needs a GPU. Inference-free SPLADE skips that step. It does all the model work upfront on your documents, so queries cost about as much as BM25. We'll talk about how that works, what you give up, and how to take it to production.
You'll learn from
Kumar Shivendu
Software Engineer, Core Team at Qdrant
Doug Turnbull (Maven)
Led teams at Shopify, Reddit, Wikipedia
Wed Sep 23·5:00 PM UTC
Tensors in the Search Engine (Why?)
RAG and Agentic search requires vector search. And increasingly multi-vector retrieval. Vespa's tensor feature implements vector search. "How are tensors better than vectors?" is a common follow-up question, but it sets the wrong frame. Instead, we'll look at how tensors work (in Vespa) and what kind of retrieval problems they help solve. Not every use-case needs it, but those who do, benefit. A lot.
You'll learn from
Doug Turnbull (Maven)
Led teams at Shopify, Reddit, Wikipedia
Radu Gheorge
Software Engineer, Vespa.ai
Mon Sep 28·5:00 PM UTC
Embeddings don't solve RAG
You've been fed a false narrative. That RAG means embeddings. Ask a retrieval engineer they'll only be one small piece of a good RAG solution. By questioning embeddings as a search solution, we'll take a chance to talk about other ways of thinking of search and retrieval. Come hang out and learn about the many ways to think about search in 2026.
You'll learn from
Doug Turnbull (Maven)
Led teams at Shopify, Reddit, Wikipedia
Tue Sep 29·5:00 PM UTC
IR Today: Theory, Practice, and Agents
LLMs and agentic search have dominated search in 2026. Daniel will give his observations after attending SIGIR, the world’s leading conference on search and information retrieval, as well as reflecting on his own experience as a practitioner.
You'll learn from
Daniel Tunkelang
Independent Search Consultant. LinkedIn / Google / Endeca alum.
Doug Turnbull (Maven)
Led teams at Shopify, Reddit, Wikipedia
Thu Oct 1·5:00 PM UTC
High scale agentic memory with Turbopuffer
Agents don't remember what they learn Jargon. Personal preferences. What works / doesn't work. In this talk, we'll explore one approach: just index all agentic traces into a high-scale vector database like Turbopuffer. Then reconstruct only the knowledge that we need, treating these neat and tidy bits of knowledge like a cache that expires, not facts that live forever. We'll discuss pros / cons of such an approach, and play with a fun solution.
You'll learn from
Doug Turnbull (Maven)
Led teams at Shopify, Reddit, Wikipedia