RAG (Retrieval-Augmented Generation) is a technique that enhances AI responses by first retrieving relevant documents from a database before generating an answer. RAG enables AIs to access up-to-date information beyond their training data.
What is RAG?
RAG combines two approaches: information retrieval (like a search engine) and text generation (like an LLM). This allows AIs to provide accurate, sourced responses.
How RAG Works
- Query: User asks a question
- Retrieval: System searches relevant documents
- Augmentation: Retrieved content is added to context
- Generation: LLM generates response using this context
RAG Implications for AEO
For your content to be retrieved by RAG systems:
- Be indexed by AI data sources
- Have clear, relevant content
- Maintain freshness and updates
- Use structured data