What Is Semantic Search?

Semantic search finds results by meaning rather than by matching exact keywords. It represents the query and the content as embeddings, numeric vectors that capture meaning, and returns the items whose meaning is closest to the query, so it can surface relevant results even when they use different words.

How semantic search works

Semantic search turns text into embeddings using a model, so that pieces with similar meaning end up close together in vector space. Content is embedded ahead of time and stored, usually in a vector database. When a query arrives, it is embedded the same way, and the system finds the stored items nearest to it, returning those as the most relevant results.

Because it matches on meaning, semantic search handles synonyms and paraphrases that keyword search misses. A search for "how to reset my password" can find a document titled "recovering account access," even with no shared words. The trade-off is that it can miss exact terms like codes or names, which is why it is often combined with keyword search in a hybrid approach.

Why semantic search matters for AI

Semantic search matters because it lets systems find information the way people mean it, rather than only the way they spell it. This is the retrieval engine behind many AI features, including the step in retrieval-augmented generation that pulls relevant context for a model to answer from. Better semantic search means more relevant results and better-grounded answers. At Custom AI Studio, semantic search is part of how we build systems that find the right information in a client's data by meaning, not only by keyword.