Retrieval-Augmented Generation
Last updated: March 2025
Definition
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, factual answer. It's how AI tools cite your content.
Why It Matters
RAG is the mechanism behind AI citations. When ChatGPT or Perplexity cites your website, it's because a RAG pipeline retrieved your page as relevant. Making your content retrievable and citable directly determines whether AI systems recommend you or your competitor.
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.