RAG vs Fine-Tuning: Which Should You Use?
Use RAG when your data changes frequently or you need citations and source grounding. Use fine-tuning when you need consistent tone, format, or domain-specific behavior baked into the model itself. Most production systems end up combining both.
What is the primary reason you're considering customization?
FAQ
Can I use RAG and fine-tuning together?+
Yes — and most mature AI applications do. Fine-tuning shapes the model's behavior (format, tone, reasoning style) while RAG provides up-to-date factual grounding. A common pattern: fine-tune on your internal communication style, then add RAG for live document retrieval.
How much training data do I need for fine-tuning?+
Quality matters more than quantity. 50–500 high-quality input→output pairs typically outperform thousands of noisy examples. Start small, evaluate on a held-out test set, and add more data only if you see consistent error patterns.
Is RAG expensive to run?+
RAG adds embedding costs (typically $0.0001 per 1K tokens with text-embedding-3-small) and vector DB costs ($0–$70/mo depending on scale). For most applications under 100K queries/month, RAG adds under $50/mo to your AI bill.
When does fine-tuning clearly beat RAG?+
Fine-tuning wins when: (1) you need the model to consistently produce a specific output schema or style that prompting can't achieve, (2) you're doing classification or extraction on a narrow domain with consistent patterns, or (3) latency is critical and you can't afford the extra retrieval round-trip.