TL;DR: RAG (Retrieval-Augmented Generation) = LLM retrieving external content to augment answers. AI Overviews, Perplexity, ChatGPT with web search. all use RAG. Understanding RAG = understanding how to be cited.
What is RAG
LLM (ChatGPT) is trained on fixed data. Doesn't know what happened today. RAG:
- User asks question.
- LLM searches external sources (web, database).
- Retrieves relevant content.
- Uses retrieved content to generate answer.
Augments generation with retrieval.
Why Matters for SEO
If you're in the retrieval pool, LLM cites you. If not, you don't exist to LLM users.
How Sources Selected
Each RAG system has own retrieval:
- AI Overviews. Google's index.
- ChatGPT Web. Bing's index.
- Perplexity. own index.
- Claude. limited web access.
Optimization for RAG
- Rank in source engine (Google for AI Overviews).
- Clear semantic content.
- Factual and cited.
- Schema.org markup.
- E-E-A-T signals.
Types of RAG
1. Web RAG
Retrieves from general web. ChatGPT browse, AI Overviews.
2. Enterprise RAG
Retrieves from company docs. Used internally.
3. Vector DB RAG
Retrieves from vector embeddings. Advanced.
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