🔎Rag combines AI models and vector databases to provide accurate information.
💡Data management and governance are essential for reliable results.
🗂️Vector databases store structured and unstructured data for easy processing.
🔄Vector embeddings are updated with new data, improving the accuracy of responses over time.
🤖Rag can be used by both users and bots to retrieve relevant information.
What is Rag or Retrieval-Augmented Generation?
—Rag is a process that combines AI models with vector databases to retrieve relevant information and generate accurate responses.
Why is data management important in Rag?
—Data management ensures that the information used for retrieval and generation is clean, governed, and accurate.
How are vector databases used in Rag?
—Vector databases store structured and unstructured data, making it easier for AI models to process and generate results.
How often are vector embeddings updated in Rag?
—Vector embeddings are updated with new data, allowing the AI models to provide more accurate responses over time.
Who can benefit from Rag?
—Both users and bots can benefit from Rag to retrieve relevant and accurate information.
00:00Rag combines AI models and vector databases to provide accurate information.
01:03Data management and governance are essential for reliable results in Rag.
02:45Vector databases store structured and unstructured data for easy processing in Rag.
03:57Vector embeddings in Rag are updated with new data, improving response accuracy over time.
04:48Both users and bots can benefit from Rag to retrieve relevant and accurate information.
