Retrieval-Augmented Generation (RAG)
- is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response
- extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model