What is semantic search and how does it work?

Semantic search represents an advanced AI-driven approach that focuses on understanding the meaning and intent behind user queries, transcending traditional keyword matching methods. Unlike conventional search engines that operate like a basic ctrl+F function across the web by seeking exact word matches, semantic search interprets user intent by converting queries into vector embeddings, which are mathematical representations. This allows the search system to identify related concepts and contextually similar content, such as linking "Italian food" to "Tuscan cuisine" or "Mediterranean restaurants." Core technologies such as vector embeddings, knowledge graphs, and transformer models power this process, enabling semantic search to provide more relevant and nuanced search results by understanding word relationships and context. This capability is particularly beneficial for AI applications, as it allows for efficient information retrieval and synthesis without the need for exact keyword matches, reducing hallucination and enhancing the reliability and accuracy of AI outputs across various industries, including e-commerce, healthcare, and finance.

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