Home > AI Solutions > Gen AI > White Papers > Dell Scalable Architecture for Retrieval-Augmented Generation (RAG) with NVIDIA Microservices > What is RAG?
Retrieval-augmented generation (RAG) represents a significant advancement in the capabilities of LLMs by incorporating an external data retrieval step into the generative process. This approach allows the model to dynamically fetch relevant information from a vast corpus of data, including user-specific datasets, enriching its responses with accurate, up-to-date, and context-specific content. RAG fundamentally enhances the utility and effectiveness of chatbots by enabling them to access and use external knowledge bases, making them more informative and versatile.
The fundamental concept of RAG lies in its ability to 'connect the dots' between the LLM's general knowledge and the user's specific data. For instance, a user can import a collection of PDFs containing domain-specific knowledge into a database that the LLM can understand. The LLM can then leverage this domain-specific knowledge to generate responses that are not only based on its inherent general understanding but also tailored to the user's specific dataset.
This approach underscores the importance of bringing AI to data rather than the other way around. It emphasizes privacy, security, and using existing data effectively within organizations. RAG enhances the model's responses by allowing a generic LLM to access and use domain-specific data, making it more informative and versatile.