Resources / Guide · 11 min read
RAG vs Fine-Tuning: A Practical Decision Guide
Pick the right architecture for the right problem — without ending up with both, neither, or the wrong one.
The short version
Use RAG when the answer needs to come from your data and that data changes. Use fine-tuning when you need the model to behave differently — tone, format, structure — regardless of input. Most teams need RAG. Almost no one needs fine-tuning.
When RAG is right
Customer support over your help docs. Internal knowledge over your wiki. Product Q&A over your specs. Anywhere the right answer is "look it up and cite it."
When fine-tuning is right
When the base model cannot reliably produce the format or tone you need, and you have hundreds to thousands of high-quality examples. Classification with a custom taxonomy. Specific JSON shapes the model keeps breaking.
The hybrid that actually wins
A small fine-tune on output format + RAG for content. The fine-tune ensures structure; RAG ensures truthfulness.
Costs and tradeoffs
RAG infra costs more per query (embeddings + retrieval). Fine-tuning costs more upfront and locks you to a model version. Plan for both.
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