Retrieval-Augmented Generation
AI systems that search the web in real-time to provide current information alongside their training data.
Retrieval-Augmented Generation (RAG) is a technique where AI systems combine their training data with real-time information retrieved from the web or other sources.
Examples of RAG-enabled AI:
- Perplexity AI (always searches the web)
- ChatGPT with browsing enabled
- Google Gemini with search
- Microsoft Copilot
RAG matters for AI visibility because:
- Your current content can influence responses (not just training data)
- Fresh content has a chance to be cited
- SEO and GEO become more interconnected
- You can see which content gets retrieved and cited
For RAG systems, traditional SEO principles matter more — ranking well means being more likely to be retrieved and cited. This is where SEO and GEO converge most closely.