Free Lesson

Vector search from zero to production at GitHub

Part of The Frontier of AI Search

60 min
Jun 24, 2026 12:45 PM
Virtual (Zoom)

In this video

What you'll learn

Plan semantic search beyond the MVP

Why early vector-search MVPs can mislead you, and what to test before production.

Debug vectors at production scale

How capacity, indexing, sharding, reindexing, and Lucene merges complicate rollout.

Tune retrieval for real users

How scoring, oversampling, num_candidates, and linear retrievers affect relevance.

Why this topic matters

Vector search looks easy in demos, but production scale exposes hidden failure modes: capacity planning, indexing bugs, quantization tradeoffs, sharding, and ranking quality. GitHub’s BBQ rollout is a useful case study in turning “just add vectors” into an operationally reliable semantic search system.

You'll learn from

David Tippett

David Tippett

Search Relevance @ Github

Trey Grainger

Trey Grainger

Author AI Powered Search

Doug Turnbull

Doug Turnbull

Co-Author AI Powered Search

Previously at

GitHub
CareerBuilder
Shopify.com
Reddit
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