When you create a retrieval augmented generation (RAG) solution, you must analyze the format and structure of the content to determine what preprocessing needs to be done. However, the content for many RAG solutions is a doc set that is actively being updated, in the process of being created, or still in the planning stage. If the content for your RAG solution is dynamic, then it can be adapted to be more accessible to generative AI models. You can improve results from RAG solutions by preparing your content for AI and repairing content that produces bad answers from AI.
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