Semantic Search
Semantic search is a search method that matches a query to relevant content based on meaning and intent, using techniques like vector embeddings, rather than requiring an exact or near-exact match of the words typed.
Semantic Search in Practice
A traditional keyword search for “gentle exfoliant for sensitive skin” might miss Northloom’s genuinely relevant page, titled “best products for reactive skin,” because the specific words don’t overlap. A semantic search system recognizes the two phrases describe the same underlying need and surfaces the page anyway.
How to Optimize for Semantic Search
There’s no single metric that isolates “semantic search performance” the way there is for click-through rate. The practical signal is whether a page ranks or gets retrieved for query variations it was never explicitly optimized for — a sign the underlying concept, not just the exact phrase, is being matched correctly.
Why Semantic Search Matters
This is the shift that made keyword-density-style optimization obsolete years before GEO existed, and it’s the same underlying principle GEO and RAG-based retrieval both build on: modern systems reward clearly communicating a concept, not repeating a specific phrase.
Terms Related to Semantic Search
Vector embeddings ·
Retrieval-Augmented Generation (RAG) ·
Entity SEO
Semantic chunking
FAQ
Does semantic search mean keywords don’t matter anymore?
Keywords still matter as a signal of topic and intent, but exact-phrase matching matters far less than it used to. Writing naturally and clearly about a concept generally outperforms forcing an exact keyword phrase into awkward repetition.