What Is Hybrid Search?

Hybrid search is a retrieval method that combines keyword search with semantic search to return better results than either alone. It matches both the exact words in a query and their meaning, so it can find results that share vocabulary and results that are related in concept but worded differently.

How hybrid search works

Hybrid search runs two kinds of search and merges their results. Keyword search, the traditional kind, matches the literal words in a query and is precise when the exact term matters, such as a product code or a name. Semantic search uses embeddings to match by meaning, catching relevant results that use different words.

Each has a weakness the other covers. Keyword search misses paraphrases; semantic search can drift from exact terms. Hybrid search scores results from both and blends them, so a query for a specific term still surfaces closely related material, and a vaguely worded question still finds the exact match when one exists.

Why hybrid search matters for AI

Hybrid search matters because real queries mix exact terms and loose intent, and neither keyword nor semantic search handles both well on its own. Combining them improves the quality of retrieval, which directly affects systems that depend on finding the right information, including the retrieval step in RAG. Better retrieval means better answers. At Custom AI Studio, hybrid search is one of the techniques we use to make retrieval systems return the right material from a client's data, rather than only the closest keyword or the loosest match.