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A common user journey for a RAG use case is constructing a dynamic prompt template that takes input on-the-fly to pass to an LLM model (e.g., "summarize these found documents: ..."). Currently, users have to manually build the prompt templates from scratch, set as a sub-value in the model config, and blindly run to see the results. We should add a few enhancements:
A common user journey for a RAG use case is constructing a dynamic prompt template that takes input on-the-fly to pass to an LLM model (e.g., "summarize these found documents: ..."). Currently, users have to manually build the prompt templates from scratch, set as a sub-value in the model config, and blindly run to see the results. We should add a few enhancements:
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