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We’re planning an internal assistant that needs to answer questions about product specifications and company policies. Those documents change every few weeks, so I’m weighing retrieval-augmented generation against fine-tuning a model.
For information that needs to stay current and be traceable to source documents, when is RAG versus fine-tuning the more sensible choice? I’d also like to know what trade-offs teams tend to underestimate, such as retrieval quality, update effort, or the model’s tendency to answer beyond the material it finds.
