Cheat at Search Essentials: Vectors and Embeddings

Hosted by Doug Turnbull

220 students

In this video

What you'll learn

Learn how embeddings implement 'semantic search'

What is a 'vector embedding' and why do they capture meaning? And how can that be used to build a search system?

Tackle the practical realities vector retrieval

Why do we need a vector database? And why are they different from traditional search engines? Should they be different?

How are vector + lexical techniques combined?

Offer a concrete and concise explanation of how you will help students understand and apply this lesson.

Why this topic matters

For decades, search techniques were dominated by lexical search. IE some form of 'dumb token matching'. Yet in the last decade, embedding based retrieval has completely upended that paradigm, giving us results focused on semantic search before strings.

You'll learn from

Doug Turnbull

Author. Builder. Search practitioner.

Doug helps search teams move beyond hype.

He led machine-learning-driven search at Reddit and Shopify, served as CTO of OpenSource Connections, and co-authored Relevant Search and AI Powered Search.

Doug has trained and advised teams at the Wikimedia Foundation, Wayfair, and AWS, and created Quepid, SearchArray, and the Elasticsearch Learning to Rank plugin.

Previously at