{"id":1591,"date":"2026-09-16T13:49:41","date_gmt":"2026-09-16T13:49:41","guid":{"rendered":"https:\/\/ouaggad.com\/blog\/?post_type=ai_term&p=1591"},"modified":"2026-09-16T13:49:41","modified_gmt":"2026-09-16T13:49:41","slug":"what-is-rag","status":"publish","type":"ai_term","link":"https:\/\/ouaggad.com\/blog\/en\/ai\/glossary\/what-is-rag\/","title":{"rendered":"What Is RAG? A Simple Guide to Retrieval-Augmented Generation"},"content":{"rendered":"

A language model may not know what is in a document it has never seen. Retrieval-augmented generation (RAG)<\/strong> addresses this by finding relevant information in an external source and giving it to the model along with the question before the model writes an answer.<\/p>\n

How does RAG work?<\/h2>\n
    \n
  1. Documents are prepared and divided into searchable passages.<\/li>\n
  2. When someone asks a question, a retrieval system finds relevant passages. It may use keyword search, numerical embeddings, or both.<\/li>\n
  3. The question and retrieved passages are sent to a language model, which generates a response based on that context.<\/li>\n<\/ol>\n

    The model is not necessarily retrained whenever documents change. Instead, the application supplies information at answer time. For example, an internal assistant might search a company handbook before answering an employee\u2019s question.<\/p>\n

    When is RAG useful?<\/h2>\n

    It is useful when information is specialized, changes over time, or should be traceable to a document. Retrieval can reduce unsupported answers, but it does not guarantee accuracy<\/strong>. The system may retrieve an outdated or irrelevant passage, and the model may still misread it. Readers should check the cited source and its date.<\/p>\n

    How is RAG different from search?<\/h2>\n

    Search normally returns results for a person to inspect. RAG adds a generation step: a model combines retrieved material into an answer. The quality of that answer depends on the quality of both retrieval and generation.<\/p>\n

    In short<\/h2>\n

    RAG combines external information retrieval with language generation. Related concepts include embeddings, context windows, and AI hallucination.<\/p>\n","protected":false},"excerpt":{"rendered":"

    What is RAG? Learn how retrieval-augmented generation combines document search with language models, when it helps, and why it does not guarantee accuracy.<\/p>\n","protected":false},"author":1,"featured_media":0,"template":"","ai_topic":[412,410],"class_list":["post-1591","ai_term","type-ai_term","status-publish","hentry","ai_topic-generative-ai-en","ai_topic-large-language-models"],"ai_facts":{"official_name":"Retrieval-augmented generation","short_definition":"A technique that retrieves relevant information from external sources and supplies it to a language model before it generates an answer.","official_url":"","status":"Active","last_verified":"2026-09-16","developer":0,"capabilities":[]},"yoast_head":"\nWhat Is RAG? 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