Information Retrieval

Information retrieval systems search structured or unstructured data sources to identify content that best matches a user's query. Unlike simple keyword matching, modern IR systems often use semantic search, vector embeddings, ranking algorithms, and relevance scoring to return the most useful results. In AI applications, retrieved information can also be provided to a language model to generate more accurate and context-aware responses.

Organizations often store vast amounts of documents, databases, and knowledge assets that are difficult to search manually. Information retrieval enables users and AI systems to quickly access relevant information, improving decision-making, productivity, and response accuracy. It is also a foundational component of Retrieval-Augmented Generation (RAG) systems.