Semantic search

Classical search matches keywords: documents containing the query tokens rank higher. Semantic search matches meaning: the query and documents are both converted to vector embeddings, and documents whose embedding is closest to the query embedding rank higher, even if they share no exact tokens. A query for "how to stop being late" can surface a document titled "time-management techniques" without any keyword overlap.

Semantic search is the dominant retrieval pattern inside answer engines. ChatGPT Search, Perplexity, Claude, Gemini, AI Overviews, and Bing Copilot all use some combination of semantic search, classical keyword search, and link-graph signals for retrieval. The exact recipe is proprietary and engine-dependent.

For publishers, semantic search changes the keyword-targeting playbook in two ways:

1. Topic coverage matters more than keyword density. A page that comprehensively covers a topic (definitions, mechanisms, examples, edge cases) will be retrieved for many related queries it never explicitly targeted. The keyword-stuffing era is decisively over.
2. Internal-link semantic clusters matter. Pages that link to and from semantically-related pages on your domain accumulate topical authority that semantic search retrieval can detect. The topical authority entry covers this in depth.

The practical implication for AEO/GEO content: write for the topic and the questions, not for exact keywords. Semantic retrieval will surface the page across the long tail of related queries.