Fine-Tuning vs RAG vs Prompting: What Each Costs

Automated excerpt

Fine-tuning adjusts the model's weights using examples of the behaviour you want. Only retrieval changes what the model knows. Then consider fine-tuning, if the failure is behaviour. A smaller fine-tuned model matching a larger general model on one narrow task can cut per-request cost substantially. Your fine-tuned model reflects the examples it saw.

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