AI & Technical
Retrieval-Augmented Generation (RAG)
Enhancing AI responses by retrieving relevant documents before generating an answer.
Retrieval-Augmented Generation (RAG) is an AI architecture that improves response accuracy by first retrieving relevant content from a knowledge base, then passing that content to the LLM as context for generating the answer. Instead of relying solely on the model's training data, a RAG-enabled agent can answer questions using your specific documentation, pricing, or product details. This dramatically reduces hallucinations and keeps responses grounded in facts you control.