Retrieval-augmented generation combines a retriever that searches an external document collection with a generator language model. Lewis et al. (2020) introduced it, pairing a parametric seq2seq model with a non-parametric vector index of Wikipedia and showing it produced more factual, specific text and set state-of-the-art results on open-domain question answering.
Most modern RAG systems retrieve with dense embeddings, following Dense Passage Retrieval (Karpukhin et al., 2020); the idea of joining retrieval to a language model was also developed in REALM (Guu et al., 2020).
What is RAG in AI? The meaning in plain terms
The RAG meaning in one line: look it up first, then answer. A plain LLM answers only from what it absorbed during training, which can be outdated or misremembered. A RAG system first fetches passages relevant to the question — from company documents, a knowledge base or the web — and gives them to the model along with the question, so the answer can quote and cite real sources.
That is why RAG AI is the usual way to build chatbots over private documents, support assistants that must stay current, and research tools that show where each claim came from.
How retrieval-augmented generation works
RAG vs fine-tuning
Both make a model more useful for your domain, but they change different things. RAG changes what the model can see; fine-tuning changes the model itself.
RAG at MultipleChat
MultipleChat Projects work this way: files you add to a project are indexed, and when you send a message MultipleChat searches them and passes only the most relevant chunks to the model — so every chat in the project can draw on your documents without copy-pasting. Web search adds live sources, and on paid plans Auto Verification can check an answer for factual issues right after it is delivered.