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Fine-tuning vs RAG

The architectural decision between teaching a model new knowledge by retraining its weights (fine-tuning) versus inserting fresh facts into its context at runtime (RAG).

What Is Fine-tuning vs RAG?

Use RAG when knowledge changes often, is large, needs citations, or is private per user. Use fine-tuning when you need consistent tone, format compliance, or domain-specific reasoning that prompting cannot enforce. Most production apps end up using both: fine-tune for style and format, RAG for facts. MCP and tool-use cover a third category: live actions.

How Groovy Web Uses This

We start every AI engagement with this trade-off framed. Most clients ship RAG first; we add fine-tuning only when tone or format quality demands it.

Need Help with This?

Our AI-First engineers build production systems using Fine-tuning vs RAG technology. Talk to us.

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