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, and you need that information available immediately. When a customer asks "what are the current withdrawal fees?", a RAG system retrieves the current policy document and grounds the answer in verified knowledge. If you fine-tuned the model on policies, you'd need to retrain weekly. With RAG, you update the knowledge base and the AI immediately has access.

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 of what makes a restaurant vs. a grocery store don't change weekly. Every millisecond of latency matters (processing payments at scale). Fine-tuning embeds knowledge into model weights—no retrieval network, just a forward pass.

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. Batch processing? RAG is fine. User-facing requests under 100ms? Fine-tuning wins. Third, explainability. RAG says "here's the source"—perfect for compliance. Fine-tuning says "the model learned this"—less explainable but more predictable.

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. This article walks through architecture patterns with real production examples.

About the Author

Durai Rajamanickam is a Business Transformation Leader and author of The AI Inflection Point: Volume 1 - Financial Services. With over two decades of experience, he specializes in AI-driven enterprise transformation, designing evidence-based ROI frameworks, and helping organizations modernize legacy systems with intelligent automation.

His work focuses on translating AI ambition into measurable business outcomes, with case studies spanning Ramp, Nubank, Coinbase, RBC, and Stripe—all showcasing AI ROI between 256% and 1,700%.

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