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What Is Retrieval-Augmented Generation, aka RAG?
- Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.
- It gives models sources they can cite, like footnotes in a research paper, so users can check any claims. That builds trust.
- Another great advantage of RAG is it’s relatively easy. Developers can implement the process with as few as five lines of code.
- With retrieval-augmented generation, users can essentially have conversations with data repositories, opening up new kinds of experiences. This means the applications for RAG could be multiple times the number of available datasets.
- Almost any business can turn its technical or policy manuals, videos or logs into resources called knowledge bases that can enhance LLMs.
- Companies including AWS, IBM, Glean, Google, Microsoft, NVIDIA, Oracle and Pinecone are adopting RAG.
- NVIDIA developed an AI Blueprint for building virtual assistants. Organizations can use this reference architecture to quickly scale their customer service operations with generative AI and RAG.
- Getting the best performance for RAG workflows requires massive amounts of memory and compute to move and process data. The NVIDIA GH200 Grace Hopper Superchip is ideal.
- In the background, the embedding model continuously creates and updates machine-readable indices, sometimes called vector databases, for new and updated knowledge bases as they become available.
- The future of generative AI lies in creatively chaining all sorts of LLMs and knowledge bases together to create new kinds of assistants that deliver authoritative results users can verify.
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