Beyond chunks - how to actually data model for RAG

Hosted by Radu Gheorghe and Doug Turnbull

Fri, Nov 21, 2025

4:00 PM UTC (1 hour)

Virtual (Zoom)

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Cheat at Search with Agents
Doug Turnbull
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What you'll learn

Why preferring large documents works best for RAG

Trade-offs between large documents with chunk arrays and chunk documents with denormalized metadata

The ideal data model for RAG retrieval

Combining chunk and document-level features into a single searchable entity

How to surface the most relevant chunks

Using rank profiles to compute chunk relevance within each document and return top N chunks for each

Why this topic matters

In enterprise and web search, many questions are answered by separate bits of documents, yet semantics and properties of the containing entity are also important. While there's no silver bullet - because data modeling is hard - we'll explore techniques to navigate the large vs small trade-off.

You'll learn from

Radu Gheorghe

Software Engineer, Vespa.ai

Radu has been in the search space for many years, mainly on Elasticsearch, Solr, OpenSearch, and, more recently, Vespa.ai. Helps users with both the relevance and the operations side of retrieval. Enjoys education in all its forms (training, blog posts, books, conferences...) and got the chance to be involved in all of them.

Doug Turnbull

Agentic Search Consultant

Doug Turnbull helps clients build better agentic search systems. He led machine-learning-driven search initiatives at Reddit, significantly improving search relevance through Learning to Rank methods. Doug also advanced e-commerce search at Shopify and served as CTO at OpenSource Connections. He co-authored the influential book Relevant Search (Manning, 2016) and created popular open-source tools, including Quepid and the Elasticsearch Learning to Rank plugin. He regularly speaks at industry conferences, making search relevance accessible to engineers.

Previously at

Vespa.Ai
Reddit
Shopify.com

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