RAG (Retrieval-Augmented Generation)

Also known as: Retrieval Augmented Generation, RAG Pipeline, Knowledge-Augmented AI

AI architecture that combines knowledge base search with LLM text generation, reducing hallucinations and improving accuracy.

RAG (Retrieval-Augmented Generation) is an AI system architecture that combines two capabilities: (1) retrieval of relevant information from a knowledge base (documents, studies, reports) via vector search, and (2) generation of accurate responses by an LLM using that retrieved information as context.

This solves one of the most critical problems with LLMs: hallucination. Instead of generating responses based only on prior training, the model first consults updated sources of truth before responding.

In market research, RAG has powerful applications: creating insight assistants that answer questions about brand study histories, allowing marketing teams to 'chat' with their consumer insights data, or generating narrative reports anchored in real survey data.

Atlantia develops RAG systems to allow its clients to access their research data conversationally and in real time.