Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture in which a model retrieves relevant documents from an external knowledge base and uses them as contextual grounding to generate an answer — rather than relying solely on training data.
RAG combines two components: a retrieval module (which searches for and fetches relevant texts) and a generative model (which produces answers based on the retrieved texts). Perplexity is a pure-play RAG system where the retrieval source is the open web.
For content creators, understanding RAG architecture matters: content that is clearly structured, easily accessible to crawlers, and concise enough to be relevant in the retrieval step has a higher chance of being included in the AI answer.
Frequently asked questions
What is RAG in practice?
Perplexity searches the web (retrieval), finds relevant texts, and lets an LLM write an answer based on those texts (generation). The result is an answer informed by both training data and fresh web sources.
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