AI Search

What Is Semantic Search?

Definition

Search that understands the meaning and intent behind a query rather than matching keywords literally — a foundation of modern and AI-powered search.

Why It Matters

Google and AI search engines no longer rank pages by keyword match — they rank by whether the page answers the underlying intent of the query. Content optimised only for exact keyword matches underperforms content that addresses the topic broadly and naturally.

How It Works

Semantic search encodes queries and documents into high-dimensional vectors (embeddings). Relevance is scored by vector similarity rather than word overlap. This lets search engines match queries like "how to fix slow website" with pages titled "improving page speed" — the wording differs, but the meaning is the same.

Real-World Example

A blog titled "Why your site loads so slowly" ranks for queries like "fix slow website", "website takes forever to load", and "why is my site slow" — none of which contain the exact title words. Semantic search connects them through shared meaning.

Quick Facts

Google introduced semantic search capabilities with the Hummingbird update in 2013
BERT and MUM updates further advanced semantic understanding
Vector databases power semantic search in modern AI and retrieval systems
Writing for topic depth outperforms writing for keyword density