What is Semantic Search?

How Semantic Search Works

Semantic search converts both queries and documents into vector embeddings using models like Sentence-BERT or OpenAI embeddings. At query time, the query is embedded and compared against stored document vectors using cosine similarity or approximate nearest neighbor search. Hybrid search combines this with traditional keyword matching (BM25) for best results. Re-ranking models then sort the combined results by relevance, often using cross-encoder models that jointly process query-document pairs.

Why Semantic Search Matters

Traditional keyword search fails when users and documents use different terminology. A search for "how to fix a slow computer" should match a document about "optimizing PC performance." Semantic search bridges this gap. It powers the retrieval step in RAG systems and is the core of modern search engines, e-commerce product discovery, and internal knowledge bases.

Practical Example

A tech company replaces their keyword-based internal wiki search with semantic search. Employees searching for "how to request time off" now find the PTO policy document even though it is titled "Leave of Absence Procedures" and never mentions "time off." Search satisfaction scores jump from 34% to 89% within a month.

Use Cases

• Enterprise knowledge bases
• E-commerce search
• RAG retrieval
• Document discovery

Salary Impact

Related Skills

Frequently Asked Questions