What Is Semantic Search? Semantic vs Keyword Search Explained

What Is Semantic Search?

Traditional search often looks for exact words or matching keywords.

But what if two sentences use completely different words but have the same meaning?

That's where Semantic Search becomes important.

Semantic Search is a search technique that understands the meaning and context of a query instead of relying only on exact keyword matching.

It commonly uses embeddings and vector similarity search to find information that is semantically related to the user's query.

Imagine you search:

"How can I reset my password?"

A keyword-based system mainly looks for words such as:

reset + password

A semantic search system can also find a document like:

"Steps to recover your account credentials."

Even though the exact words are different, the meaning is similar.

A typical workflow looks like this:

User Query → Embedding Model → Query Vector → Similarity Search → Relevant Results

The documents are also converted into embeddings and stored in a vector database.

When the user searches, the system compares the query vector with document vectors and retrieves the most relevant results.

Semantic Search is widely used in:

A common misconception is:

"Semantic Search completely replaces keyword search."

Not necessarily.

Modern search systems often combine keyword search, semantic search, metadata filtering, and ranking techniques to provide better results.

This approach is often called Hybrid Search.

Keyword Search focuses on words. Semantic Search focuses on meaning.

This ability to understand meaning is one of the key technologies behind modern AI-powered search and RAG systems.