Fine-tuning takes a pre-trained LLM and nudges its weights on input/output pairs you provide — think "here are 500 of our best-performing briefs, learn to write more of these." The result is a custom model that defaults to your style or format without needing the examples in every prompt.
For agencies, fine-tuning is less useful than it sounds. Frontier models are already good at most tasks; once you account for the cost of curating data, running the training, and maintaining the model as the base improves, the return is rarely there.
Where fine-tuning shines: very specific output formats (a strict JSON schema, a tone that prompting cannot quite hit), domains with private jargon, and cases where prompt length is a problem. Otherwise, start with prompt engineering and RAG and only reach for fine-tuning when you have run out of cheaper options.