What is RAG (Retrieval-Augmented Generation)?
TL;DR
Retrieval-Augmented Generation (RAG) is an AI technique where a language model retrieves information from external sources (websites, databases, knowledge bases) before generating a response.
Definition
Retrieval-Augmented Generation (RAG) is an AI technique where a language model retrieves information from external sources (websites, databases, knowledge bases) before generating a response. This allows AI assistants to provide current, accurate answers rather than relying solely on training data.
Why It Matters
RAG is how modern AI search works. When you ask ChatGPT or Perplexity for a business recommendation, they use RAG to retrieve relevant web content, then synthesize an answer. Understanding this helps you structure content to be easily retrieved and used by RAG systems.
Examples
- Perplexity.ai retrieving web pages about dentists in Edmonton, then synthesizing a recommendation with citations
- ChatGPT's web search feature using RAG to answer questions about current events or local businesses
Frequently Asked Questions
How does RAG affect my business?
RAG-based AI assistants retrieve web content in real time when answering user questions. If your business information is well-structured, consistent, and authoritative across the web, RAG systems are more likely to retrieve and recommend you.
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