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RAG vs Fine-Tuning: When to Use What in 2026
Nanostack
A decision framework for choosing retrieval-augmented generation, fine-tuning, or both — with cost, latency, and maintenance trade-offs.
The question every AI team asks in week one
RAG keeps knowledge fresh without retraining. Fine-tuning embeds style, format, and domain vocabulary into the model itself. Most production systems use both — but the ratio matters.
Choose RAG when…
- Source documents change weekly (policies, pricing, product docs).
- You need citations and auditability for regulated industries.
- You want to swap the base model without rebuilding training pipelines.
Choose fine-tuning when…
- Output structure is rigid (JSON schemas, medical coding, legal clauses).
- Latency budgets are tight and you can afford a smaller specialized model.
- Brand voice and tone must be consistent without long system prompts.
Hybrid stack (what we recommend)
RAG for facts, lightweight adapters for format, and a strong eval suite tying them together. Nanostack builds these stacks with clear ownership boundaries so your team can maintain them — explore our AI development services.