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RAG and fine-tuning solve different problems. This comparison helps product and engineering leaders choose the right lever — and avoid paying for the wrong one.
Use RAG when answers must reflect changing, permissioned knowledge. Use fine-tuning when you need durable behavior or style that prompting cannot lock in. Many production systems use both.
RAG requires ingestion pipelines, search quality, and ACL design. Fine-tuning requires quality datasets, training workflows, and careful regression testing when base models change.
RAG shifts cost to retrieval infrastructure and inference-time context. Fine-tuning shifts cost to data prep and training cycles, then usually cheaper prompts — until you retrain.