Stop Fine-Tuning. Start Prompting.
Fine-tuning is the hammer that makes everything look like a nail. For 90% of use cases, better prompting gets you further, faster, and cheaper.
Every week, someone asks me whether they should fine-tune a model for their use case. My answer is almost always the same: probably not.
When fine-tuning makes sense
Fine-tuning is the right call when you need a model to adopt a very specific output format, when you're processing thousands of requests per second and need to use a smaller model, or when your task is so domain-specific that general models genuinely can't handle it. That covers maybe 10% of real-world use cases.
The prompting-first approach
For everything else, structured prompting with few-shot examples, chain-of-thought reasoning, and clear constraints will get you 95% of the way there.in hours instead of weeks, at zero training cost.
The trick is treating your prompt like production code. Version it. Test it. Iterate on it systematically. Most people write a prompt once, get mediocre results, and conclude they need fine-tuning. What they actually need is prompt engineering discipline.
A well-crafted prompt is worth more than a fine-tuned model.
Start with prompting. Exhaust every optimization. Then.and only then.consider fine-tuning. Your wallet and your iteration speed will thank you.
Written by
Dayssam Bakaar