Model Fine-tuning is useful when:
You want the model to understand domain-specific knowledge (e.g., medical, legal, financial).
You want better performance on a specific task (e.g., translation, summarization, code generation).
You need the model to match a specific tone and style (e.g., formal writing, brand voice).
You need higher accuracy than prompt engineering or beddings can provide.
But Model Fine-tuning is not needed when:
Your task can be solved with prompt engineering or one-shot/ few-shot examples.
You only need to filter/classify content (embedding models + classifiers may suffice).