When Fine-Tuning Makes Sense (And When It Doesn't)

What fine-tuning actually does (and doesn't do)

Fine-tuning teaches a model to follow patterns. You show it hundreds or thousands of examples - "given this input, produce this output" - and it learns to replicate that behavior.

What fine-tuning is good for:

• •Format consistency: You need JSON with specific fields. Always. No exceptions. Fine-tuning bakes that in.
• •Style matching: Your brand has a specific voice. Fine-tuning can make the model sound like your existing content.
• •Domain terminology: Your industry uses words differently than general English. Fine-tuning internalizes that.
• •Reduced prompt size: Instead of 2,000 tokens of instructions, you can use 50. Lower costs, faster responses.

What fine-tuning can't do:

• •Add knowledge: Fine-tuning doesn't teach facts. It teaches patterns. For new knowledge, you need RAG or a different approach.
• •Fix bad data: If your training examples are inconsistent, your model will be inconsistent.
• •Replace reasoning: Fine-tuning won't make a model smarter. It makes it better at following demonstrated patterns.

The real investment (it's the data)

The actual fine-tuning process is the easy part. Upload data, click a button, wait. The hard part is creating training data that's actually good.

You need examples. Lots of them. And they need to be:

• •Representative: Cover the variety of inputs you'll actually see in production.
• •Consistent: If two examples show conflicting patterns, the model learns confusion.
• •High-quality: The model learns exactly what you show it. Poor examples produce poor results.
• •Sufficient: Most providers need at least 50-100 examples for basic tasks, often hundreds for complex ones.

Try prompt engineering first. Seriously.

Before committing to fine-tuning, spend real time on prompting. Most "prompt engineering doesn't work" complaints come from people who tried for an afternoon.

1. Few-shot examples in the prompt. Show 3-5 examples of the exact input/output format you want. This alone solves most consistency issues.
2. Structured output modes. Use JSON mode or function calling. Let the API enforce structure instead of hoping for it.
3. System prompt refinement. Be specific. "You are an assistant" means nothing. "You analyze financial reports and output JSON with these exact fields" means something.
4. Break down complex tasks. One prompt doing ten things will fail. Ten prompts doing one thing each usually works.

If you've genuinely tried all of this and you're still not getting consistent results, fine-tuning might be the answer.

The cost math

Fine-tuning can reduce per-call costs by shortening prompts. But training has upfront costs, and fine-tuned models cost more per token than base models.

The math works out when:

• •You're making enough calls that prompt token savings add up to more than training costs.
• •Your current prompts are genuinely long (1,000+ tokens of instructions).
• •You're running the same task repeatedly with minor input variations.

The math doesn't work when:

• •Low volume. If you're making a few hundred calls a month, prompting is almost always cheaper.
• •Your prompts are already short. You can't save what you're not spending.
• •Tasks vary significantly. Fine-tuning locks in patterns - if every call is different, that's a problem.

Decision framework

Ask yourself these questions:

1. Have I genuinely exhausted prompt engineering options?
2. Do I have (or can I create) 100+ high-quality training examples?
3. Is my task repetitive enough that learned patterns will generalize?
4. Will I make enough API calls for the cost math to work out?
5. Do I have someone who can maintain and iterate on the model over time?

If you answered yes to multiple questions, fine-tuning is the right approach. We handle the complexity of data preparation, training, and ongoing optimization so your team stays focused on your product.