Embeddings
AI Fundamentals
Embeddings are lists of numbers that capture aspects of the meaning of text, images, or other data, so software can find items with similar meaning.
SEO & AI Search
What Is Semantic Search?
Semantic search is a way of searching that interprets the meaning and intent behind a query, and the meaning of the content being searched, rather than only matching the exact words typed. It can recognize synonyms, related ideas, and natural phrasing, so a search for “how to speed up an old laptop” can surface an article titled “Fix a slow computer.” You see it in web search engines and in the search boxes of many websites and business tools.
Also called meaning-based search
Example
An outdoor gear retailer’s on-site search only matches keywords. A shopper types “jacket that keeps me dry on a rainy hike.” The results show a fleece jacket whose description happens to include “hike,” while the store’s rain shells, described as “waterproof hardshells,” never use the words “dry” or “rainy,” so they rank below it or don’t appear at all.
With semantic search, the system compares the meaning of the query with the meaning of the product descriptions and finds the closest matches. It can connect “keeps me dry” and “rainy hike” with “waterproof hardshell,” so the rain jackets rise toward the top. The retailer might keep keyword matching in the mix for exact queries, such as a specific model name or item number, so shoppers who already know what they want still find it quickly.
Where it shows up and how it works
Semantic search shows up in two main places.
- Web search engines. Engines such as Google Search use language-understanding systems to interpret what a query means, recognize the people, places, and things it mentions, and match it to pages that address that meaning, even when the wording differs.
- Search inside websites and business tools. Product catalogs, help centers, and internal document search often rely on embeddings, which are numerical representations of meaning. Content is converted into embeddings ahead of time and stored in a vector database or in the vector index of an existing search engine or database. When someone searches, the query is converted the same way, and the system returns the items whose meaning is closest.
Two neighboring ideas are easy to mix up with it. Search intent is the goal behind a query, such as learning, comparing, or buying; semantic search helps a system return results that fit that goal by interpreting what the query means, not just its words. AI search tools write an answer instead of only listing results, and they often rely on semantic search behind the scenes to find their sources.
Keyword, semantic, and hybrid search
Keyword search, sometimes called lexical search, looks for the words in the query within the text being searched. It is fast, predictable, and precise for exact terms such as names, part numbers, and error codes, but it can miss results that use different wording.
Semantic search handles synonyms, paraphrases, and conversational questions better, but it can be less precise when the exact string matters. A search for one specific model number might return similar models instead of the one the person typed.
That is why many systems use hybrid search: they run keyword and semantic matching together and merge the two sets of results into one ranked list. Many systems then add a re-ranking step that reorders the top results by relevance. Hybrid approaches are a common choice for product catalogs, help centers, and the retrieval step of retrieval-augmented generation (RAG) systems, where an AI assistant looks up relevant passages before writing an answer. Web search engines also combine word matching with meaning, along with many other signals.
What it means for your content
Because semantic search matches meaning, you don’t need to repeat an exact keyword phrase over and over, or create a separate page for every small variation of a phrase. Search systems can often connect “cheap,” “affordable,” and “budget” on their own. What matters more is whether your content clearly and thoroughly addresses the topic and the questions people actually ask.
- Write naturally. Use the words your customers use, in full sentences that make sense to a reader.
- Cover the topic thoroughly. Address the related questions, terms, and details a knowledgeable reader would expect to find.
- Answer real questions directly. Clear, specific answers are easier for any search system to match to a query.
- Keep exact terms where they matter. Product names, model numbers, and technical terms should still appear exactly as people search for them.
Where semantic search falls short
Semantic search helps with many kinds of searches, but it does not guarantee good results on its own.
- Close is not always correct. Results can be related in meaning but wrong for the task, such as a similar product in the wrong size or a help article for a different version of your software.
- It depends on your content. If descriptions are thin, outdated, or inconsistent, the system has little meaning to match against.
- It is harder to explain. With keyword search, you can see which words matched. With semantic search, it can be less obvious why one result ranked above another, which makes troubleshooting slower.
- It needs testing and upkeep. Teams typically test with real queries from their search logs, adjust the balance with keyword matching, and update the stored embeddings when content changes.
Semantic search vs. keyword search
| Semantic search | Keyword search | |
|---|---|---|
| What it matches | The meaning and intent of the query | The words and phrases in the query |
| Synonyms and phrasing | Often connects different wording with the same meaning | Often handles word forms (hike, hiking) but misses different wording unless synonyms are added |
| Typos and vague queries | Handles loose, conversational queries; typo tolerance varies | Struggles with vague queries; typos need extra features such as spell correction |
| Precision for exact terms | Can return close but wrong matches for names, codes, or item numbers | Strong for exact names, part numbers, and codes |
| Typical technology | Language models, embeddings, and vector search | A word index that ranks results by matching terms |
| Best use | Natural-language questions, discovery, and varied wording | Known items, exact phrases, and technical identifiers |
Many search systems combine both approaches, which is known as hybrid search.
Further reading
- AI ImplementationA starting point if you want AI connected to your own systems, covering process redesign, system connections, automation, and deployment.
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