When should you use RAG versus fine-tuning to customize LLM behavior? What are the trade-offs, and how do you decide which approach fits a given use case?

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RAG and fine-tuning serve different purposes for customizing LLM behavior, and understanding their trade-offs is essential for choosing the right approach for each use case. RAG connects the model to external data sources, retrieving relevant information at inference time.