Fine-tuning vs Prompting — Which Should You Use?
Prompting is fast and free; fine-tuning permanently adjusts model behavior. Knowing when to choose each saves you thousands of dollars and weeks of work.
TL;DR: Prompting is fast and free; fine-tuning permanently adjusts model behavior. Knowing when to choose each saves you thousands of dollars and weeks of work.
Prompting: Steering with Instructions
Prompting means writing instructions that guide the model's behavior at inference time. System prompts, few-shot examples, chain-of-thought — all are prompting techniques. Zero cost, instant iteration, works with any model out of the box.
Fine-tuning: Changing the Weights
Fine-tuning retrains a base model on your custom dataset, baking new behaviors into the model weights. The result: lower inference latency (shorter prompts needed), consistent style, and the ability to learn data unavailable at pre-training.
Decision Framework
Choose prompting when: you're prototyping, your task is simple, or data changes frequently. Choose fine-tuning when: you have 1,000+ high-quality examples, need consistent format/tone across thousands of calls, or prompting has failed after serious effort.
The "Prompt First" Rule
OpenAI, Anthropic, and Google all recommend: exhaust prompting before fine-tuning. Most tasks that seem to need fine-tuning can be solved with better prompts — system instructions + examples in context + chain-of-thought reasoning.