Category: AI Search & Retrieval
Definition:
Retrieval-Augmented Generation (RAG) is a method that allows an AI system to retrieve relevant information from external sources and use that information when generating an answer.
Instead of relying only on information contained in the model itself, a RAG system can search a connected collection of documents or data and use relevant results as context.
Why it matters:
RAG helps AI systems provide answers based on current, specific, or domain-relevant information. It also helps explain why the content and sources available to AI retrieval systems can influence what appears in AI-generated answers.
For AI visibility, being discoverable and useful within retrieval systems can be an important part of the broader picture.
Example:
A company has a detailed knowledge base about its software. An AI assistant receives a question about a specific feature, retrieves the relevant documentation, and uses it to generate an answer.
RAG vs. LLM:
An LLM generates and understands language based on its training.
RAG adds a retrieval step, allowing the system to bring relevant external information into the generation process.
Related terms:
LLM · AI Search · Retrieval · Vector Search · Embeddings · Grounding · AI Citations · Knowledge Base · AI Visibility
In simple terms:
RAG is a way for AI to find relevant external information and use it to produce a better-informed answer.
