Retrieval-augmented generation, or RAG, is a technique that makes large language models far more reliable for business use. Instead of trusting the model's trained-in memory, RAG pulls the most relevant passages from your own documents and drops them into the prompt, so the model answers from your data.
The result is an answer grounded in trusted, current, company-specific information rather than the model's vague general knowledge. That is how you build a support assistant that answers from your own documentation, or an internal tool that quotes your own policies.
RAG goes straight at the two biggest weaknesses of an LLM: making things up, and not knowing anything recent or private. For anything where accuracy matters, grounding the model with RAG is the highest-impact step you can take, and it is usually the first one worth taking.