What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that combines information retrieval with AI text generation. It first retrieves relevant information from a knowledge base or document collection, then uses that information to generate more accurate, up-to-date, and contextually relevant responses.

When to implement RAG?

RAG is valuable for building AI assistants with access to specific knowledge bases, creating chatbots for customer support, enabling AI to work with private documents, or when you need AI responses grounded in specific, current information.

When might RAG be challenging?

RAG implementation can be complex when dealing with large, diverse knowledge bases, ensuring retrieval quality, managing system latency, or when the retrieved information conflicts with the model's training.

What is the importance of RAG in AI/UX?

RAG enables AI applications to provide more accurate, current, and trustworthy information by grounding responses in retrieved facts, significantly improving user confidence and the practical value of AI systems.