What is the difference between semantic search and lexical search?

Lexical search matches the words in a query to the words on a page. Semantic search matches meaning, so it can connect a query and a page that use different words for the same idea. Systems that combine the two are using hybrid search.

How does semantic search power AI answer engines?

AI engines use semantic search to find passages that match the meaning of a question before they write an answer. That lets them pull in a passage even when it's worded differently from the question, and the answer is then written from those passages.

What is semantic search in SEO?

In SEO, semantic search means optimizing for how search engines understand topics and intent instead of chasing exact keyword matches. In practice, that comes down to covering a subject clearly and answering the questions people ask about it. It also means a page can rank for searches whose wording it never uses, as long as it answers the intent behind them.

Do keywords still matter with semantic search?

Yes. Using the words your audience uses still helps search engines understand a page. What's changed is that repeating a keyword adds little, while answering the related questions adds a lot.

What Is Semantic Search? How It Works and Why It Matters

Definition

Semantic searchSemantic search is a way of finding information by matching the meaning of a query instead of its exact words. It lets a search engine or AI assistant return a page about "affordable CRM for startups" when someone asks for a "cheap customer database for a new company," even though the two phrases share almost no words.

How semantic search works

Traditional keyword search, also called lexical search, looks for pages that contain the words in a query. Search "running shoes for flat feet" and it finds pages with those words, then ranks them on signals like how often and where the words appear.

Semantic search works with meaning. The process runs in three steps:

A language model converts each passage of content into embeddings, which are lists of numbers that represent what the text is about. Passages with similar meanings end up with similar numbers.
When someone searches, their query gets converted the same way.
The system compares the query's embedding with the stored passages and returns the ones closest in meaning. This matching step is usually called vector search.

Because the match happens on meaning, "running shoes for flat feet" can pull up a passage about "stability trainers for overpronation," even though the two phrases share only one word.

Semantic search vs keyword search

	Keyword (lexical) search	Semantic search
Matches on	Exact words and close variants	Meaning and intent
Handles synonyms	Only if they're programmed in	Naturally
Handles long, conversational queries	Poorly	Well
Best at	Exact product names and model numbers	Questions phrased in many different ways

Each approach catches things the other misses. Keyword search is dependable for product names and model numbers, while semantic search handles questions better, so many systems run both together. That combination is called hybrid search.

Semantic search in AI answer engines

Semantic search has become more important for marketers because it sits inside how AI engines such as ChatGPT and Perplexity put their answers together.

When an AI engine answers with live sources, it usually follows a pattern called retrieval-augmented generation: it retrieves relevant passages first, then writes the answer from them. Semantic matching is a big part of how those passages get picked. Engines often split a question into several related searches before retrieving anything, a process known as query fan-out, and each of those searches gets its own passages.

That has two consequences for brands. First, engines work with passages, so a page is usually split into smaller sections before it's matched. A section that answers one question on its own is easier to retrieve than an answer spread over several paragraphs (more on this in content chunking). Second, your page doesn't need the exact phrase someone typed. It needs to address the idea behind that phrase clearly.

What semantic search means for your content

Keyword density does nothing for semantic search. What helps is making the meaning of each section easy to pick out:

Answer one question per section. Give the section a heading that states the question or topic, and answer it in the first sentence or two.
Cover the related questions. If people who ask about a topic also ask about cost and alternatives, answer those as well. They're the same follow-up searches an AI engine runs during query fan-out.
Use specific language. Name the product, who it's for, and the problem it solves. Vague marketing copy gives a retrieval system very little meaning to match.
Keep facts consistent across your site. If two pages describe what you do differently, any system will have a harder time understanding you.

How to see whether semantic search is working for you

Retrieval happens inside the engine, so you can't watch it directly. You can measure the outcome, though: whether AI engines cite your pages and mention your brand when buyers ask questions in your category. Searchable keeps the answers behind each tracked prompt, including the pages each engine cited, so you can see which of your sections get picked up and which questions you're missing.

Frequently asked questions

Largely, yes. Google has used meaning-based systems for years, including its Knowledge Graph and, since 2019, the BERT language model, to work out what a query is about beyond its exact words. Keyword signals still play a part alongside them.

See how AI engines talk about your brand

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EmbeddingsEmbeddings are lists of numbers that represent the meaning of a piece of content, such as a paragraph of text or an image. Content with similar meaning gets similar numbers, so a system can find a relevant passage even when it shares no words with the question. Embeddings are what make semantic search and AI retrieval work.Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique where an AI model first searches a set of documents for passages that match a question, then writes its answer from those passages. Because it looks things up at answer time, the model can use up-to-date information and point to where it came from.Content chunkingContent chunking is the way AI search systems split a web page into smaller passages so each one can be retrieved and quoted on its own. The term also describes writing pages in clear, self-contained sections, so any passage an engine pulls out still makes sense without the rest of the page.Vector searchVector search finds content by meaning instead of exact words. It turns text into embeddings, lists of numbers that represent what the text means, then returns the stored items whose embeddings sit closest to the query's. That's how a search for "cheap flights" can find a page about "budget airfare."Hybrid searchRetrieval that combines keyword matching with vector search, so results match both the exact terms and the meaning of a query.Query fan-outWhen an AI engine splits one question into several related searches, retrieves results for each, and combines them into a single answer.

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