RAG: When to Use It
~10 min read
RAG is the right tool when you need the model to answer from a custom knowledge base, but its vocabulary and writing style don't need to change — knowledge injection without behavior change.
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Key points
- •RAG fits the quadrant: high external knowledge need, low need to change the model's vocabulary/style
- •It assumes the model's default writing/reasoning style is already appropriate — only the knowledge is missing
- •Updating the underlying documents automatically updates future answers, with no retraining required
- •This makes RAG especially well suited to dynamic or frequently-changing information
- •RAG alone can't make the model adopt unfamiliar vocabulary or a different writing style — that gap belongs to fine-tuning