Retrieval-Augmented Generation
Last updated: March 2025
What is Retrieval-Augmented Generation?
RAG. A technique where AI models pull real-time information from external sources before generating a response.
Instead of relying only on training data, the model searches a database or the web, retrieves relevant documents, and uses them to produce a grounded answer. It is one mechanism behind many AI citations.
Why It Matters
RAG is one mechanism behind AI citations. When an answer engine cites your website, a retrieval pipeline may have selected your page as relevant. Making your content retrievable and citable can improve citation readiness, but it does not guarantee recommendation placement.
How to Improve
- Write content with clear, extractable claims. Short paragraphs with one idea each are easier for retrieval systems to index.
- Use descriptive headings that match common questions. RAG systems match queries to content via semantic similarity.
- Publish original data and unique insights. RAG pipelines prioritize content that adds information not found elsewhere.
- Keep content updated. Retrieval systems de-prioritize stale information when fresher alternatives exist.