RAG (Retrieval-Augmented Generation)
RAG is a pattern where an AI model retrieves relevant documents from a knowledge base and uses them as context to generate a response.
RAG (Retrieval-Augmented Generation) is the dominant production pattern for grounding LLM responses. The system retrieves relevant documents from a knowledge base, provides them as context to the LLM, and generates a response grounded in those documents, rather than relying on the model's parametric memory.
In context
RAG is the standard architecture for production AI customer service. The system embeds the customer's question into a vector representation, searches the knowledge base for the most-similar articles, retrieves the top-N matches, and includes them in the LLM's prompt. The model then writes a response grounded in those retrieved articles.
The pattern dramatically reduces hallucinations: ungrounded LLMs hallucinate in 15-30% of customer-service responses, while RAG-grounded systems with verification pipelines can run below 1% hallucination rate in published benchmarks.
RAG quality depends entirely on KB quality. Brainfish research puts "over 80% of traditional knowledge bases out of date." An LLM grounded on a stale KB produces fluent wrong answers with high confidence, the failure mode that the POV essay "Your KB is not a knowledge system" describes in detail.
How Auralis uses RAG (Retrieval-Augmented Generation)
Auralis runs RAG with the Knowledge Center as the system of record. The Auralis team continuously closes KB gaps (detected via Audit), so the RAG pipeline runs on current, accurate, and complete documents, not on the customer's pre-existing KB-debt.