Why I Stopped Fine-Tuning and Started Prompt-Chaining

The Turning Point

I spent three weeks fine-tuning a model for customer support classification. The results were... okay. 87% accuracy. Then I tried a simple prompt chain: one call to extract intent, another to classify, a third to generate the response.

Result: 94% accuracy. Zero training data. Two hours of work.

When Fine-Tuning Makes Sense

Don't get me wrong — fine-tuning has its place: - When you need consistent output formatting at scale - When latency matters (single call vs. chain) - When you have genuinely unique domain knowledge

The Prompt-Chain Advantage

For most tasks, prompt-chaining gives you: 1. Debuggability — inspect each step 2. Flexibility — swap models per step 3. Speed to iterate — no training loop

The best part? You can always fine-tune later if the chain proves the concept.