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Usually not, and almost never first. Most "the model got it wrong" problems are retrieval or prompt problems — the right information was never put in front of the model, so training it harder does not help. Fine-tuning earns its place when you need a consistent output format or a narrow house style at scale, and even then it sits on top of good retrieval; that scope belongs on our Custom LLM Development page.