Definition
Vector Search uses embeddings - numerical representations of text meaning - to find semantically similar content. Unlike keyword search, vector search understands meaning: 'buy a link' and 'backlink acquisition' are recognized as semantically close. This technology is at the core of AI engines, RAG systems, and new Google search features. For SEO, it reinforces the importance of semantic coverage and search intent over simple keyword optimization.
Key Points
- Search by meaning rather than exact keywords
- Technology at the core of AI engines and RAG
- Reinforces the importance of semantic coverage for SEO
Practical Examples
Google semantic search
Google uses embeddings to understand that a page about 'how to get quality inbound links' answers the query 'effective link building strategy', even without exact keyword match.
RAG with vector search
A RAG system uses a vector database to find the most semantically relevant passages from a corpus before generating a response.
Frequently Asked Questions
No, both coexist. Modern engines combine vector (semantic) and keyword (lexical) search for optimal results. This is called hybrid search.
Cover a topic in depth with rich, varied vocabulary. Use natural synonyms and focus on search intent rather than exact keyword repetition.
Go Further with LemmiLink
Discover how LemmiLink can help you put these SEO concepts into practice.
Last updated: 2026-02-07