Semantic Search
Semantic search is an information retrieval technique that goes beyond keyword matching to understand the intent and contextual meaning of search queries. It uses natural language processing (NLP) and machine learning to interpret the semantics of words and phrases, enabling more accurate and relevant search results. This approach is fundamental in modern search engines, recommendation systems, and AI-powered applications.
Developers should learn semantic search when building applications that require intelligent search capabilities, such as e-commerce platforms, content management systems, or chatbots, to improve user experience by delivering contextually relevant results. It is particularly valuable in domains with complex queries, multilingual content, or ambiguous terms, as it reduces reliance on exact keyword matches and enhances discovery. Mastering semantic search is essential for working with large language models (LLMs), vector databases, and AI-driven search solutions.