Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an artificial intelligence (AI) framework that enhances the capabilities of generative AI models, particularly large language models (LLMs), by incorporating external knowledge sources into their response generation process. RAG works by combining retrieval-based techniques with generative-based AI models, where retrieval-based models are adept at sourcing accurate and relevant information, and generative models excel at producing coherent and contextually appropriate text[1][2][3].

The primary goal of RAG is to improve the quality, accuracy, and reliability of responses generated by LLMs. It does this by retrieving data from external sources of knowledge, such as databases, document repositories, or the internet, to fill in gaps in the model’s knowledge. These gaps may arise due to the limitations of its initial training data or the need for up-to-date information.[2][3].

RAG is particularly useful for reducing the occurrence of “AI hallucinations,” which is when an AI model generates false or misleading information. By grounding the LLM’s responses in external, verifiable data, RAG helps ensure that the information provided is current and accurate. This improves the quality of the responses and increases user trust, as the sources of information can be cited, much like footnotes in a research paper[2][3].

Implementing RAG can be relatively straightforward and cost-effective compared to continuously retraining a model with new datasets. It allows for the hot-swapping of new sources and can be applied to a wide range of applications, including chatbots, customer service, and specialized knowledge domains like healthcare and finance[2][3].

The NVIDIA GH200 Grace Hopper Superchip, with its substantial memory and compute capabilities, is cited as an ideal platform for RAG workflows due to its ability to process massive amounts of data efficiently, which is crucial for the retrieval and integration of external knowledge into LLMs[2].

Citations:

[1] https://www.cohesity.com/glossary/retrieval-augmented-generation-rag/

[2] https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/

[3] https://www.techtarget.com/searchenterpriseai/definition/retrieval-augmented-generation

[4] https://www.oracle.com/artificial-intelligence/generative-ai/retrieval-augmented-generation-rag/

[5] https://research.ibm.com/blog/retrieval-augmented-generation-RAG

[6] https://www.nightfall.ai/ai-security-101/retrieval-augmented-generation-rag