Semantic Search

Simple Definition

Semantic search is a way of searching that understands the meaning behind your words rather than just matching the exact keywords you typed. It figures out what you’re asking about and returns results that are conceptually relevant, even if they use different words.

Traditional keyword search: finds results that contain your exact search terms. Semantic search: finds results that match the intent and meaning of your query.

A Simple Analogy

Keyword search is like a librarian who looks for books with your exact words in the title. Semantic search is a librarian who understands what you’re trying to learn and recommends all relevant books, regardless of how the titles are worded.

Keyword Search vs. Semantic Search

	Keyword Search	Semantic Search
How it works	Matches exact words	Matches concepts and meaning
Query: “car won’t start”	Finds “car won’t start”	Also finds “vehicle ignition failure,” “dead battery troubleshooting”
Handles typos	Poorly	Better
Handles synonyms	No	Yes
Handles context	No	Yes

How Semantic Search Works

Semantic search uses embeddings, numerical representations of meaning. Both your query and all documents in the database are converted into these numbers. Similar meanings produce similar numbers. The search finds documents whose embeddings are closest to your query’s embedding.

This is why semantic search works even when the exact words don’t match. It’s matching concepts, not characters.

Where Semantic Search Is Used

AI chat tools searching a knowledge base before answering
Enterprise document search
Customer support finding relevant help articles
E-commerce product search
Legal research tools
Internal company wikis and intranets

Semantic Search and RAG

Semantic search is the retrieval step inside most RAG (Retrieval-Augmented Generation) systems. When you ask an AI a question and it looks through your documents to find the answer, it uses semantic search to find the right sections, then reranking to narrow them down further.