When Fine-Tuning Makes Sense: A Developer's Guide

Fine-tuning solves specific, measurable problems: models that produce inconsistent JSON schemas, inference costs that scale beyond your budget, prompts so complex they hurt performance, and specialized behavior that's impossible to achieve through prompting alone. This guide walks through the concrete benefits of fine-tuning, helps you identify which goals matter for your use case, and shows you how to get started with a clear path to measurable results. We'll cover the real use cases where fine-tuning makes sense—and when it doesn't. Let's start with the problems it actually solves: Improve Quality "Quality" means different things for different tasks. You should already have evals set up for the quality metrics you care about (if not, check out our evals guide). Fine-tuning excels in specific quality areas: Task-Specific Quality Score Most products have an overall quality metric—often a 1-5 star rating. Fine-tuning can improve this metric by teaching the model how to respond th ... Read full article.