Fine-tuning works best when you need consistent behavior, not a store of changing facts:
- Style and tone: a support voice, legal register, brand personality, or strict reading level, applied every time without prompt gymnastics.
- Format adherence: always-valid JSON against a schema, specific markdown structures, diff formats, or domain-specific notations that prompting produces.
- Domain vocabulary and conventions: using clinical, legal, or internal jargon correctly and preferring your organization's terminology.
- Task specialization: classification, extraction, routing, summarization in a fixed shape, where a tuned small model often beats a prompted large one.
- Prompt compression: baking a 3,000-token instruction block into the weights, cutting cost and latency on every request.
- Latency and cost: distilling a narrow capability from a frontier model into a 4-9B parameter model.
- Implicit rules: behaviors that are easy to demonstrate with examples but hard to articulate as instructions, like judgment calls in moderation.
- Reliability of tool calling in agentic workflows.
The common thread: you have examples of what good looks like, the task distribution is reasonably stable, and you can measure.
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