What is semantic search?
Semantic search finds results based on meaning rather than exact keywords. Traditional search matches the words you typed against the words on a page. If you write "cancel my plan" but the page says "end your subscription", keyword search misses it. Semantic search turns both your query and the content into embeddings, lists of numbers that capture meaning, and then finds the closest matches. Words no longer have to line up. The ideas do.
In plain words
Keyword search is a librarian who only finds books with your exact words on the cover. Semantic search is a librarian who actually knows what the books are about, so when you ask for "something to fix a leaky tap", they hand you the plumbing guide even though it never says "leaky tap". It matches what you mean, not what you said.
When to use it
- Help centres and docs. Customers rarely use your exact wording. Semantic search returns the right article anyway.
- Internal knowledge. Find the right document across wikis, tickets, and chat without remembering the precise title.
- The retrieval step in RAG. Pull the most relevant snippets from your data, then hand them to a language model to write the answer.
- Recommendations and dedup. Group similar products, articles, or records that say the same thing in different words.
Common pitfalls
- Close in meaning is not always correct. Semantic search returns related content, not verified facts. For exact terms, codes, or names, keyword search still wins, so many teams combine both (hybrid search).
- It is only as good as the embedding model. A model that does not know your domain or language will rank results poorly. Test on your real queries.
- Change the model, re-index everything. Embeddings from different models are not comparable. Swap the model and your old index silently stops matching.
Related articles:
- What are embeddings? - The numbers that let a computer compare meaning, the engine behind semantic search.
- What is a vector database? - Where embeddings are stored so you can search millions of them fast.
- What is Retrieval-Augmented Generation (RAG)? - How semantic search feeds your own data into an AI's answers.
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