How RAG Grounds AI Answers in Relevant Information
A usually does not search a database each time it answers a question. It creates an answer from language patterns learned during training, so its knowledge may be old, incomplete, or poorly matched to the question.
RAG first retrieves relevant information from documents, , or trusted sources. It then adds that material to the prompt so the model can produce an answer grounded in the supplied information.
This can reduce and improve accuracy, but it does not guarantee a correct result. The outcome still depends on the source documents, method, prompt, and model.
Key points
- normally answer from learned patterns rather than searching a database each time.
- RAG retrieves information, adds it to the prompt, and then asks the model to answer.
- Documents, , and trusted external sources can supply the retrieved material.
- Accuracy depends on the documents, method, prompt, and model.
- RAG can reduce , but it cannot remove them completely.