What Is Semantic Search? How Is It Different from Keyword Search?

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.

What Is Semantic Search?

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.

Keyword Search vs Semantic Search

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.

How Does Semantic Search Work?

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.

Where Is Semantic Search Used?

Semantic Search is widely used in:

• AI-powered search

• RAG systems

• Customer support

• Knowledge bases

• Document search

• Recommendation systems

• E-commerce search

Interview Tip

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.

Key Takeaway

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.