RAG is the what. Agentic Search is the how.

Hosted by Doug Turnbull

338 students

In this video

What you'll learn

Good RAG isn't chunking + vector databases

RAG fails because we've been taught a myopic view that misses out on the lessons of Information Retrieval

Agentic Search gets RAG unstuck

We think RAG means "LLMs that search". That's a half step. Real progress comes from search agents that self correct

How RAG should actually be built - from first principles

From reasoning to building a good harness - the craft of building a good RAG runs through agentic search - not vector DB

Why this topic matters

RAG has become a quagmire. Teams think it means chunking documents into a vector index. Teams that rode this hype curve have plateaud with this single retrieval pattern. Modern, agentic RAG breaks free. It's about agents that leverage the breadth of information retrieval from BM25 to late interaction. It means thoughtful tool design. And a harness that guides retrieval carefully.

You'll learn from

Doug Turnbull

Retrieval Strategist - Ex-Reddit, Ex-Shopify

Doug Turnbull has been working on natural language search since 2013 when he build medical expert search to help doctors. He's continued that work at Reddit and Shopify, participating in early RAG systems Reddit Answers and Shop.ai. After, he rebuild Daydream search away from the classic RAG architecture - towards one that combined query understanding and vector retrieval.

Doug blogs frequently and coaches teams+students at Apple, AWS, Microsoft, and more in agentic search+retrieval

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