RAG vs. Fine-Tuning in Large Language Models: A Comparison

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Top Highlights

• RAG excels when your application demands information from outside sources like databases or documents

• RAG combines the efficiency of data search (extracting apt information from expansive datasets) with the finesse of language models (creating fluent text grounded in the provided context)

• Finetuning involves refining an existing model for a specific task or dataset

• Finetuning, on the other hand, leans more on existing knowledge