When should a developer consider fine-tuning a model versus using prompt engineering to achieve the desired behavior?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Start with prompting — it's faster, cheaper, and reversible. Only move to fine-tuning when you have hundreds or thousands of examples and need consistent style or behavior that prompts can't reliably deliver.
Full explanation below image
Full Explanation
The standard recommendation is to start with prompt engineering before considering fine-tuning. Prompt engineering is fast, flexible, and costs no upfront training expense. Fine-tuning requires labeled training data, training time, and ongoing hosting of fine-tuned model weights. It's most valuable when you need to: bake in consistent formatting or style that's impractical to describe in prompts, reduce token usage by eliminating lengthy few-shot examples, or improve performance on specialized domains where you have extensive training data. Option A reverses the recommended workflow. Option C is wrong — they have very different cost, complexity, and flexibility trade-offs. Option D invents a false limitation on fine-tuning availability.