Retrieval-Augmented Generation (RAG) finds relevant information from an external collection and gives it to a language model before the model answers. It connects generated text to sources that the model did not memorize during training.
A typical system searches company documents, product manuals, or other approved data for the user's question. It places the best passages in the prompt and asks the model to answer from them.
RAG helps with private, detailed, and changing knowledge. It can also support citations and easier updates. It does not guarantee truth, because retrieval can miss the right passage and the model can still misunderstand or ignore good evidence.
Rewriting in plainer words…
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