Retrieval-Augmented Generation (RAG)

Published 2026-02-10 in Deep Learning

Retrieval-Augmented Generation (RAG) is a technique that improves large language models by combining them with an external knowledge source. Instead of relying only on what the model was trained on, RAG first retrieves relevant information from databases, documents, or search systems and then uses that information to generate more accurate, up-to-date, and reliable responses . The RAG pipeline works in three main steps: an input query is received from the user, a retriever finds the most relevant information related to that query, and a generator (LLM) combines this retrieved data with its language understanding to produce the final answer. Because of this combination, RAG is widely used in chatbots, question-answering systems, search engines, and document generation , where correctness and context are crucial. Overall, RAG helps reduce hallucinations, improves factual accuracy, and enables AI systems to work with real-world, dynamic data.

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