Understanding meaning, not just words
Traditional keyword search looks for documents that contain the exact words in your query. If you search for "vehicle safety laws," it returns only documents that contain those three words. A document that uses "car crash regulations" or "automotive safety standards" would be missed, even if it is exactly what you need.
Semantic search understands the meaning of your query and finds documents that convey the same idea, even if they use completely different words. It can recognize that "affordable housing options," "budget-friendly apartments," and "low-cost living spaces" are all asking about the same thing. It handles synonyms, paraphrases, abbreviations, and even cross-language queries naturally.
How semantic search works under the hood
Semantic search works by converting everything, both documents and queries, into embeddings (lists of numbers that encode meaning). At setup time, every document in your database is processed by an AI model and stored as an embedding in a vector database. At search time, the user's query is converted into an embedding using the same model, and the vector database finds the documents whose embeddings are mathematically closest to the query's embedding.
From the user's perspective this happens instantly: the AI model converts the query to an embedding in about 10 milliseconds, the vector database finds the nearest matches in another 5 milliseconds, and results appear. Typos, informal phrasing, and varied vocabulary are all handled without any special configuration.
When to use semantic search versus keyword search
Semantic search excels at open-ended, exploratory queries where the user is looking for something by concept: "articles about reducing machine downtime," "emails about the Q3 budget discussion," or "customer feedback mentioning delivery problems."
Keyword search (BM25) still has an edge for precise lookups: a specific product code, a person's name, a legal citation number, or any case where the user knows exactly what term to search for. Neither approach is universally better. Most production systems benefit from combining both through hybrid search, which handles conceptual queries with the semantic component and exact-term queries with the keyword component.