Free Lesson

Real-time Query Refinement with Control Vectors

Part of The Frontier of AI Search

60 min
Jun 19, 2026 3:00 PM
Virtual (Zoom)

In this video

What you'll learn

What are “control vectors”?

How to steer query embeddings on the fly toward refined contexts and personalized user interests.

The “vector math” behind control vectors

Applying the well-known “king - man + woman = queen” approach to generate vector embeddings with updated meanings.

How control vectors improve vector search experiences

See how the UX and relevance of vector search are enhanced by integrating control vectors into query interpretation.

See a live e-commerce search experience and code walkthrough

We'll walk though the source code + demo a live implementation of control vectors using Vespa.ai with Vectra embeddings.

Why this topic matters

Semantic search on embeddings can return low precision results due to a lack of explicit filtering (as in lexical search). Can we solve this by letting users directly inject inclusions/exclusions and updated meaning into their query vectors? Piotr, Trey, & Philippe will introduce “control vectors” for vector search, showing the vector math, code, and a demo of a novel e-commerce search experience.

You'll learn from

Trey Grainger

Trey Grainger

Author, AI-Powered Search

Piotr Kobziakowski

Piotr Kobziakowski

AI Search Engineer specializing in search, analytics & personalization

Philippe Bouzaglou

Philippe Bouzaglou

Technical Founder at Vectra, the AI foundation model for e-commerce search

Worked at

Vespa.Ai
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Elastic
Gorillas
Searchkernel
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