Understand the Basics of RAG

RAG, short for Retrieval-Augmented Generation, is a way to make language models give better answers by letting them look things up before they reply. First, the system turns the user’s question into a search query and scans a knowledge source, such as a set of documents or a database. It then pulls back the most relevant passages, called “retrievals.” Next, the language model reads those passages and uses them, plus its own trained knowledge, to write the final answer. This mix of search and generation helps the model stay up to date, reduce guesswork, and cite real facts. Because it adds outside information on demand, RAG often needs less fine-tuning and can handle topics the base model never saw during training.