Semantic Search is a search approach that finds results based on meaning and intent, rather than just matching keywords. It understands what you mean, not only the exact words you typed.
What it means in plain English
Traditional keyword search looks for pages containing your exact words. Semantic search goes deeper: using embeddings, it captures the meaning behind your query and finds content that matches that meaning, even if the wording is completely different. This produces more relevant results and handles synonyms, context, and natural-language questions far better.
It is the search technology behind many modern AI-powered tools.
A simple example
Search “affordable places to eat nearby” and semantic search understands the intent and surfaces “budget restaurants in your area,” even though few of the exact words match — because it matches meaning, not just keywords.
Why it matters
Semantic search makes finding information more intuitive and accurate, and it is a core component of retrieval-augmented generation, where an AI must find the most relevant information to answer a question. It represents a shift from matching words to understanding meaning.
Related terms
- Embedding — the technology that powers semantic search.
- Vector Database — stores embeddings for semantic search.
- Retrieval-Augmented Generation — relies on semantic search.
Frequently asked questions
What is semantic search?
It is search that finds results based on meaning and intent rather than exact keyword matches, using embeddings to understand what you mean.
What powers semantic search?
Embeddings and vector databases: content and queries are turned into vectors, and results are found by matching meaning, which is also central to retrieval-augmented generation.
