Semantic search is a data retrieval technique that focuses on understanding the contextual meaning and intent behind user queries rather than relying solely on exact keyword matching. It operates within the broader fields of natural language processing (NLP), information retrieval, and AI-powered search systems, becoming foundational to modern applications like Retrieval-Augmented Generation (RAG), recommendation engines, and enterprise knowledge bases. Unlike traditional lexical search (BM25, TF-IDF) which matches literal terms, semantic search maps queries and documents into high-dimensional vector embeddings that capture semantic relationships—enabling it to find "laptop for programming" when a user searches "computer for coding." A critical insight: hybrid approaches combining semantic and lexical signals nearly always outperform either method alone in production systems, as they balance semantic understanding with exact term matching.

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