"What is semantic search and how does it work"

"Semantic search finds results based on meaning and intent rather than exact keyword matches — it understands that \"cheap flights\" and \"affordable airfare\" are the same query. It's powered by embeddings and is the foundation of modern AI search, recommendation systems, and enterprise knowledge retrieval."

What Is Semantic Search and How Is It Different From Keyword Search

Semantic search finds results based on what you mean, not just the words you type. Search for “how to reduce employee turnover” and a semantic search engine will return documents about “staff retention strategies” and “reducing attrition” — even if those exact words never appear in your query.

Keyword search can’t do this. If the document doesn’t contain your keywords, it doesn’t show up. That’s been the fundamental limitation of enterprise search for decades, and it’s why most company knowledge bases are frustrating to use.

How It Works

Semantic search uses embeddings — numerical representations of meaning generated by AI models. Here’s the process:

Your documents are converted into embeddings and stored in a vector database. Each document (or chunk of a document) becomes a list of numbers that represents its meaning.
When a user searches, their query is converted into an embedding using the same model.
The system finds documents whose embeddings are mathematically closest to the query embedding — meaning the documents that are most semantically similar to the question.

The result: search that understands synonyms, paraphrases, and conceptual relationships without anyone manually configuring them. You don’t need to build a thesaurus or keyword mapping. The AI model learned these relationships from the data it was trained on.

When a Business Should Use It

Internal knowledge management. This is the most immediate ROI opportunity. Your company wiki, documentation, policies, Confluence pages — employees spend hours searching for information that keyword search can’t find because they don’t know the exact terminology. Semantic search fixes this.

Customer support. Help customers find answers in your knowledge base by describing their problem in natural language. “My screen goes black when I plug in a monitor” should match an article titled “External Display Troubleshooting” — semantic search makes this connection.

E-commerce product search. “Comfortable running shoes for flat feet” should return relevant products even if no product description contains that exact phrase. Semantic search understands intent and matches it against product attributes and descriptions.

Document retrieval for RAG. If you’re building a RAG application — connecting an AI model to your documents — the retrieval step is semantic search. The quality of your RAG system depends directly on the quality of your semantic search.

What to Watch Out For

Keyword search still has its place. If someone searches for a specific product SKU, order number, or exact phrase, keyword search is faster and more precise. The best production search systems are hybrid — combining semantic search for meaning-based queries with keyword search for exact matches. Don’t rip out keyword search entirely.

Quality depends on the embedding model. Semantic search is only as good as the embeddings powering it. A general-purpose embedding model may not understand industry-specific jargon, internal acronyms, or domain terminology. Test with real user queries from your domain before committing to a model.

Relevance isn’t the same as accuracy. Semantic search can return results that are topically related but don’t actually answer the question. “How do I cancel my subscription” and “Our subscription plans” are semantically related, but only one answers the question. Tuning relevance — through reranking, filtering, and result quality scoring — is an ongoing process.

Don’t underestimate the data prep. Your documents need to be chunked intelligently, cleaned of boilerplate, and structured in a way that produces meaningful embeddings. A semantic search engine pointed at a messy, unstructured document dump will return messy, unhelpful results.

The Verdict

Semantic search is a meaningful upgrade over keyword search for any application where users express their needs in natural language — which is most applications. The technology is mature, the tooling is accessible, and the ROI for internal knowledge management alone usually justifies the investment.

Start with a hybrid approach: layer semantic search on top of your existing keyword search rather than replacing it. This gives you the meaning-based matching you’re missing while preserving exact-match precision for structured queries.