Semantic search matches on what a query means rather than the exact words it uses.
Old search matched strings. Type "cheap CRM" and it hunted for pages with the words "cheap" and "CRM" on them. Semantic search works on meaning instead. It turns both the query and your content into mathematical representations of their sense and looks for the closest fit, so a page about "affordable customer-relationship tools for small teams" can answer "cheap CRM" while sharing almost none of the words.
This is the logic sitting under modern AI answers. When a RAG system goes to fetch passages to ground a reply, it's almost always semantic search deciding what counts as relevant.
It's also why keyword stuffing is finished as a tactic. Repeating a phrase does nothing when the engine is matching sense instead of tallying occurrences, and it can actively hurt by making the page read like spam to the signals that judge quality. What wins is content that covers an idea, and the questions around it, thoroughly and in plain language. The upside is that you can end up cited for wordings you never literally wrote, as long as the page genuinely speaks to the intent behind them. Someone asks how to "cut my SaaS churn" and lands on your page about "improving customer retention," zero shared keywords, because the two mean the same thing. A string-matching engine would have walked straight past it.
Related terms
- Retrieval-Augmented Generation (RAG): leans on semantic search to find passages
- Generative Engine Optimization (GEO): being findable by meaning
- Answer Engine Optimization (AEO): getting cited once you're retrieved