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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 information found through specific and relevant data sources.
- RAG is used to link generative AI services to external sources like technical details or other relevant data sources.
- RAG helps models to become more trustworthy and accurate, making them more efficient in delivering authoritative answers.
- With RAG, one can have conversations with data repositories, which opens up new applications for the advent of AI.
- RAGs relatively easy set up empowers developers with the ability to implement it with as few as five lines of code, till date.
- Companies such as AWS, IBM, Google, Oracle, Microsoft, and Pinecone are adopting RAG for LLMs.
- NVIDIA AI Blueprints enables developers to build pipelines that connect AI applications to enterprise data using industry-leading technology.
- The NVIDIA LaunchPad lab provides developers and IT teams with hands-on training on how to build AI chatbots with RAG.
- RAG can potentially enhance customer service operations, employee training, and developer productivity.
- The future of generative AI lies in agents with knowledge bases that can dynamically orchestrate to create autonomous assistants with authoritative and verifiable results for users.
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