How do you evaluate tradeoffs between fine-tuning vs RAG in LLMOps?

Fine-tuning modifies model behavior permanently, while RAG injects external knowledge dynamically without retraining.

Fine-tuning is best for learning behavior patterns, tone, or domain-specific reasoning. RAG is better for frequently changing knowledge. Fine-tuning requires training infrastructure and risk of overfitting, while RAG depends on retrieval quality and latency. Many production systems combine both approaches.