Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation, or RAG, combines a language model with an external knowledge store to ground outputs in up-to-date or proprietary information. The system retrieves relevant documents, injects them into the prompt, and then generates text. For AI designers and growth teams, RAG supports accurate Ai Messages based on real product data, policies, and content libraries, reducing hallucinations and aligning messaging with current facts. Retrieval-augmented generation, often abbreviated RAG, combines a language model with an external knowledge store that is searched at query time. Relevant documents are injected into the prompt so the model can ground its outputs in up-to-date or proprietary data. For AI messaging, RAG reduces hallucinations and ensures replies reflect real product information, policies, and content assets.