Semantic Search
Also known as: meaning-based search, vector search
Semantic search retrieves content by meaning rather than by matching exact keywords. It compares the intent behind a query with the meaning of stored passages using vector embeddings, so a page can be found even when it never uses the searcher's exact words. It underpins how AI systems locate the material they cite.
Semantic search retrieves content by meaning instead of by exact word matching. Rather than looking for pages that contain a specific phrase, it compares the intent behind a query with the meaning of your passages and returns the closest matches. This is why a well-written page can surface for a question it never literally repeats, and why keyword stuffing has lost most of its old power.
Why it changed what gets surfaced
Under keyword matching, you competed by repeating the exact terms a searcher might use. Semantic search reads the underlying idea, using vector embeddings to measure how close your content sits to the question. The practical effect is that clear, complete coverage of a concept beats mechanical phrase repetition. It is also the retrieval layer that feeds AI answers, working hand in hand with vector databases and passage ranking.
How to write for it
Aim for meaning that is unmistakable.
- Answer the actual question early and plainly.
- Cover the surrounding concepts a curious reader would ask about.
- Define the entities so the topic is unambiguous.
Do this and your content matches a wider range of real questions. For a fuller playbook, see optimizing content for AI search and how to get cited by AI.
Frequently asked questions
How is semantic search different from keyword search?
Keyword search matches the literal words in a query against the words on a page. Semantic search compares meaning, using embeddings, so it can match a question to relevant content even when the wording is completely different. It is more forgiving of how people actually phrase things.
How do I write for semantic search?
Write directly and cover the concept fully rather than stuffing keywords. State the answer clearly, define the entities involved, and address the real questions people ask. Clear meaning is what gets matched, not exact phrase repetition.
Does keyword optimization still matter?
It helps, but it is no longer enough on its own. Semantic systems reward content that genuinely covers a topic. Keywords signal relevance, but clarity and completeness are what win retrieval.
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