What is semantic search?
Semantic search is search that matches meaning and intent rather than exact words, so a query and a page can match even when they share almost none of the same phrasing.
A search for "best budget laptop" and a page titled "affordable laptop under $500" mean close to the same thing, even though they don't share a single significant word. Older, keyword-based search would miss that overlap. Semantic search finds it by converting both the query and the page into a numeric representation of what they mean, then comparing how close those representations are, rather than comparing the literal text.
That same underlying mechanism is what decides which passages get pulled in to ground an AI-generated answer. It's not a separate, newer technology bolted onto AI search, it's the same idea search engines have been using for years, applied to picking source material instead of picking result links.
The practical implication is the opposite of what people assume: optimizing for semantic search isn't about stuffing in synonyms or keyword variants to widen the net. It rewards writing that states a complete, clearly worded idea, because that produces the strongest, least ambiguous meaning-match, which is also just what makes writing good.
Related
- Retrieval-augmented generation (RAG)Retrieval-augmented generation is the technique of retrieving relevant documents and giving them to a model as context before it answers.
- GroundingGrounding is when a model answers using documents retrieved at the time of the question, rather than only from what it absorbed during training.
- Keyword intentKeyword intent is the underlying goal behind a search query, whether the person wants to learn something, reach a specific site, compare options, or buy right now.
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