Free Lesson
AI Search Fundamentals: Vector Spaces, Matching, & Ranking
Part of Exploring Modern AI Search
75 min
Mar 6, 2026 10:30 AM
Virtual (Zoom)
In this video
What you'll learn
Sparse Vector Spaces and Lexical Search
How does keyword matching work with an inverted index (in contrast with embeddings), and what is a sparse vector space?
Dense Vector Spaces and Semantic Search
How does search over embeddings work with flat/ANN indexes? What are the many kinds of dense vector spaces that exist?
Vector Similarity, TF-IDF / BM25, and relevance ranking
How do we score how "relevant" documents are for a given query? We'll cover cosine similarity and TF-IDF / BM25 scoring.
An overview of contemporary and emerging AI Search methods
We'll summarize the more advanced AI Search methods we'll be teaching in the full AI-Powered Search course
You'll be ready for the AI-Powered Search course!
This session provides all prerequisite knowledge needed for the AI-Powered Search course (please join us!).
Why this topic matters
This session covers foundational concepts in information retrieval around matching and ranking. We'll cover sparse vs. dense vector spaces, lexical (BM25) vs. semantic (embedding) search, and matching and relevance ranking fundamentals.
This session is free (open to all) and also provides the prerequisite background needed for anyone to join Trey & Doug's month-long "AI-Powered Search" course.
You'll learn from
Doug Turnbull
Principal ML Engineer in Search
Trey Grainger
Author, "AI-Powered Search", Founder @ Searchkernel
Built Search At
