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

Summary

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 through examples. Improve Style Conformance A customer service chatbot for a bank needs a very different style and tone than a fantasy roleplaying agent. Fine-tuning enforces specific styles more effectively than style-prompting. Better JSON Formatting We've seen JSON formatting accuracy jump from under 5% to over 99% with fine-tuning, when compared to the same untuned base-model. When you need a model to produce output in a specific JSON format, smaller models often struggle out of the box. Even if your model produces valid JSON, it commonly produces the wrong schema (incorrect key names, missing required fields, etc). Fine-tuning significantly improves LLMs' ability to produce valid JSON in the correct schema. The same applies to other formats like function calls, XML, YAML, and markdown. Lower Cost and Faster Speed Fine-tuning is a great way to make your AI app faster and cheaper. Fine-tuning for Shorter Prompts Prompts grow quickly as you add details needed to perform a task (task descripti...