RAG vs Fine-Tuning: When to Use Each

RAG and fine-tuning are often presented as alternatives. They're actually complementary, and picking wrong costs months in production.

Retrieval-Augmented Generation (RAG) is your answer when you need real-time updates, latency penalties are acceptable, and explainability matters. Coinbase's customer support system works with RAG because policy changes happen weekly.

Fine-tuning excels when patterns are stable, consistency matters, and you can't afford retrieval latency. Stripe's merchant classification works with fine-tuning because the patterns don't change weekly.

The decision tree: First, ask about update frequency. If knowledge updates faster than monthly, consider RAG. If it's stable for quarters, fine-tuning works. Second, latency. User-facing requests under 100ms? Fine-tuning wins. Third, explainability. RAG says "here's the source"—perfect for compliance.

The hybrid approach: Use RAG for changing knowledge, fine-tune for stable patterns. Coinbase doesn't just use RAG—they fine-tune the retrieval system itself to better understand support queries.

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