Build a vector database from scratch

Hosted by Doug Turnbull and John Berryman

Wed, May 7, 2025

4:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

401 students

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Go deeper with a course

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

Learn how embedding similarity search works

Learn the most common graph-based vector search algorithm used in Elasticsearch, Weaviate, QDrant, Pinecone, etc

See where vector search goes wrong

As you watch an expert live-code, see where the algorithm falls apart as a real-life expert makes a mistake

Deepen your vector search knowledge

Dense vectors provide specific constraints on retrieval solutions - learn what these are, how they can go wrong

Why this topic matters

RAG systems all use vector databases. HNSW (Hierarchical Navigable Small Worlds) is the most common algorithm. If you want to build RAG, you should appreciate how this algorithm works

You'll learn from

Doug Turnbull

Search Consultant and Coach

Doug has done embedding-based retrieval since using Latent Semantic Indexing to generate search synonyms in 2013. Author of Relevant Search + AI Powered Search, he now helps teams build RAG and search applications. Previous work includes leading search at Reddit, Shopify, and several AI Startups..

John Berryman

Founding member Github Copilot

John Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of Prompt Engineering for LLMs (O'Reilly). Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of Relevant Search (Manning), a book that distills his expertise in the field. John feels fortunate to have worked at the intersection of cutting-edge AI applications and foundational search technologies, giving him the opportunity to contribute to innovation in both LLM applications and information retrieval.

Previously at

Reddit
Shopify.com
Wikipedia
O'Reilly Media

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