Retrieval-Augmented Generation (RAG) is the dominant pattern for AI customer service chatbots and AI-powered knowledge bases. Instead of relying on the language model’s training data (which may be outdated or generic), the system retrieves the most relevant content from your knowledge base, then prompts the AI to answer using only that retrieved content — typically with citations back to the source articles.
RAG dramatically reduces hallucinations (AI inventing plausible-sounding but wrong answers), keeps responses current (when you update the knowledge base, the AI updates too), and creates auditability (you can see which articles the AI used). The architecture has become baseline for AI customer service portals.