Fine-Tuning vs. Prompting: A Decision Framework for Production LLMs
· 8 min read
Most teams reach for fine-tuning too early or too late. The ones who fine-tune too early burn weeks on a training pipeline before realizing a better system prompt would have solved the problem. The ones who wait too long run expensive 70B inferences on millions of repetitive tasks while accepting accuracy that a fine-tuned 7B model could have beaten—at a tenth of the cost.
The decision is not about which technique is "better." It's about matching the right tool to your specific constraints: data volume, latency budget, accuracy requirements, and how stable the task definition is. Here's how to think through it.
