{"id":14030,"date":"2025-10-06T06:48:56","date_gmt":"2025-10-06T06:48:56","guid":{"rendered":"https:\/\/www.nizamuddeen.com\/community\/?p=14030"},"modified":"2026-04-04T07:46:10","modified_gmt":"2026-04-04T07:46:10","slug":"rag-retrieval-augmented-generation","status":"publish","type":"post","link":"https:\/\/www.nizamuddeen.com\/community\/semantics\/rag-retrieval-augmented-generation\/","title":{"rendered":"What is Retrieval Augmented Generation (RAG)?"},"content":{"rendered":"\t\t
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<\/span>What Is Retrieval-Augmented Generation (RAG)?<\/span><\/h2>

Retrieval-Augmented Generation (RAG) is a system design where a model retrieves relevant context from an external knowledge base and then generates an answer using that retrieved evidence. Instead of relying purely on parametric memory, the model behaves like a search engine + writer<\/strong> in one loop.<\/p><\/blockquote>

In practice, RAG is the \u201cAI version\u201d of ranking with evidence: retrieve candidates, refine, then respond\u2014similar to how Google forms a SERP from candidates and relevance signals.<\/p>

Core definition, in semantic terms:<\/strong><\/p>