RAG: retrieval augmented generation

Retrieval-augmented generation (RAG) is a technique for improving the performance of large language models by allowing them to access external knowledge sources. This can be a useful way to improve the accuracy of a model, as it allows the model to access information that is not in its training data.

RAG is a key component of any successful AI strategy, as it can help to improve the performance of your models.