In this podcast, we have explored the key differences between RAG (Retrieval-Augmented Generation) and KAG (Knowledge-Augmented Generation). We learned that RAG fetches information on the fly from external sources during text creation, making it adaptable for current data. On the other hand, KAG has its knowledge embedded directly into the model through training, providing more consistent and accurate results, especially in specialized areas. To understand which approach in the “RAG vs KAG” discussion best suits your needs for AI-driven text generation, listen to this to know more about their strengths and weaknesses.