What is retrieval-augmented generation (RAG)?

Retrieval-Augmented Generation (RAG) is an AI technique that combines information retrieval with language generation. Instead of relying only on the AI model’s built-in knowledge, a RAG system first retrieves relevant information from trusted sources—such as websites, databases, or documents—and then uses that information to generate a more accurate, up-to-date, and contextually relevant response.

Benefits of RAG include:

• Provides more accurate answers by using current and relevant information.
• Reduces AI hallucinations by grounding responses in retrieved content.
• Improves factual reliability through trusted external sources.
• Enables access to up-to-date information without retraining the AI model.
• Supports domain-specific knowledge, such as company documentation or product catalogs.
• Can include citations or references, increasing transparency and trust.
• Enhances Answer Engine Optimization (AEO) by making well-structured, authoritative content easier for AI systems to retrieve and use.

For businesses, creating clear, accurate, and well-organized content increases the likelihood that RAG-powered AI systems will retrieve and reference it when answering users’ questions.