remixtures, Portuguese
@remixtures@tldr.nettime.org avatar

: "There are two reasons why using a publicly available LLM such as ChatGPT might not be appropriate for processing internal documents. Confidentiality is the first and obvious one. But the second reason, also important, is that the training data of a public LLM did not include your internal company information. Hence that LLM is unlikely to give useful answers when asked about that information.

Enter retrieval-augmented generation, or RAG. RAG is a technique used to augment an LLM with external data, such as your company documents, that provide the model with the knowledge and context it needs to produce accurate and useful output for your specific use case. RAG is a pragmatic and effective approach to using LLMs in the enterprise.

In this article, I’ll briefly explain how RAG works, list some examples of how RAG is being used, and provide a code example for setting up a simple RAG framework." https://www.infoworld.com/article/3712860/retrieval-augmented-generation-step-by-step.html

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