Ai Search
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where AI systems retrieve real-time information from the web or a database before generating a response, supplementing training data with current, relevant sources. RAG powers Perplexity, ChatGPT Browse, and Google AI Mode.
Why Retrieval-Augmented Generation (RAG) Matters for SEO
RAG is what makes AI search able to cite current sources — it's not all just training data. Being accessible to RAG retrievers means allowing AI crawlers and having indexable content. RAG systems evaluate source quality in real time, so authority and content clarity directly determine selection.
How Retrieval-Augmented Generation (RAG) Works
User query triggers the AI to retrieve relevant documents from the web or a vector database. Retrieved content is injected into the AI's context window alongside the query. The AI generates a response grounded in retrieved sources, often citing them inline. This is real-time, not historical.
Common Mistakes
- Blocking AI crawlers — RAG systems can't retrieve what they can't access
- Outdated content that gets deprioritised by freshness-aware retrieval
- Unstructured content that is hard for retrievers to extract cleanly
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