Build a vector database from scratch

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Build a vector database from scratch

Hosted by Doug Turnbull

Wed, May 7, 2025

4:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

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363 students

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Cheat at Search with LLMs

Doug Turnbull

View syllabus

Wed, May 7, 2025

4:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

363 students

Invite your network

Go deeper with a course

Cheat at Search with LLMs

Doug Turnbull

View syllabus

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..

Previously at