Semantic Search
Last updated June 11, 2026
What is Semantic Search in simple terms?
In simple terms, semantic search finds what you mean, not just the words you typed. Search for lowering your electricity bill and it can surface a page about cutting energy costs — same idea, different words.
What is Semantic Search?
Semantic search is a way of searching that matches on meaning rather than exact words, so a query returns relevant results even when they use different wording — because the system compares the underlying ideas, not just the letters.
Old-fashioned search is literal: it looks for pages containing the exact words you typed, so if you search for "car" it might miss a perfect result that only ever says "automobile." That's keyword search, and it stumbles whenever people describe the same thing in different words — which is most of the time. Semantic search works differently. It tries to understand the meaning behind your query and find content that means the same thing, even when none of the words match. Ask it for "a film about a shark terrorizing a beach town" and it can find the right movie without you naming it, because it grasps the concept rather than hunting for a string of letters.
The trick that makes this possible is embeddings — a way of turning text into a list of numbers (a vector) that captures its meaning, so that passages about similar ideas end up with similar numbers. Your query gets converted into one of these number-fingerprints, and the search compares it against the fingerprints of all the content to find the closest matches by meaning. That closeness is usually measured with cosine similarity, and the fingerprints are typically kept in a vector database built to find nearest matches fast. The upshot is a search that tolerates synonyms, rephrasing, typos, and roundabout descriptions, because it's comparing concepts rather than spelling. It's the same idea that lets retrieval-augmented generation fetch the right documents to feed an AI.
Semantic search powers a great deal of what now feels like "smart" search — better site search, more relevant recommendations, and the document-retrieval step behind many AI assistants. It isn't strictly better than keyword search at everything, though: when you want an exact match — a specific product code, an error number, a precise name — literal matching is more reliable, because semantic search's willingness to find "similar" things can drift past the exact thing you meant. That's why many real systems blend the two, using keywords for precision and semantics for understanding, to get the best of both.
Real-world example of Semantic Search
Imagine searching your company's help center for "my laptop won't turn on." A keyword search might return nothing useful if every relevant article says "device fails to power up" or "black screen on startup" — none of which share your words. Semantic search bridges that gap: it converts your phrase into a meaning-fingerprint and finds those articles anyway, because they're about the same problem. You get the troubleshooting guide you needed despite not guessing the exact wording the writers used. That tolerance for saying things in your own words — rather than having to match the system's vocabulary — is exactly what makes semantic search feel like it actually understands the question.
Related terms
Frequently asked questions about Semantic Search
What is the difference between semantic search and keyword search?
Keyword search matches the literal words you type, returning pages that contain those exact terms. Semantic search matches meaning, so it can find relevant results that use entirely different wording — synonyms, rephrasings, or descriptions. Keyword search is more reliable when you need an exact match like a product code or precise name; semantic search is better when people describe the same idea in varied ways. Many systems combine both, using keywords for precision and semantics for understanding.
How does semantic search work?
It converts text into embeddings — lists of numbers that capture meaning, so similar ideas get similar numbers. Your query is turned into one of these number-fingerprints, and the system compares it against the fingerprints of all the content, usually measuring closeness with cosine similarity, to find the passages that mean the most similar thing. Those fingerprints are typically stored in a vector database designed to find nearest matches quickly. Because it compares concepts rather than spelling, it tolerates synonyms, rephrasing, and typos.
What is semantic search used for?
It powers search and discovery that needs to understand intent: smarter website and document search, product and content recommendations, question-answering over a knowledge base, and the retrieval step in AI assistants that fetch relevant documents before answering. It's especially valuable wherever users phrase things in their own words rather than matching a fixed vocabulary. For exact-match needs — specific codes, names, or numbers — it's often paired with keyword search so you get both precise hits and meaning-based ones.