Developer argues fine-tuning is often unnecessary and costly first step

A developer argues that fine-tuning is frequently an expensive and premature solution for underperforming AI features. In many cases, the underlying model is capable, but the issue lies with inadequate context, such as poor data retrieval or contradictory instructions. The article emphasizes that fine-tuning a model on flawed data only yields more confident, but still incorrect, results without addressing the root cause. It recommends a sequence of first improving prompts and data retrieval, establishing a robust evaluation set, and only then considering fine-tuning for specific, stable tasks.