Expert advises against fine-tuning AI models as initial solution

An AI expert argues that fine-tuning models is often an expensive and ineffective first step to address performance issues. The core problem is usually a lack of proper context due to flawed retrieval systems, contradictory instructions, or poorly formatted inputs, which fine-tuning does not fix. The article recommends first improving prompts and retrieval, and establishing a robust evaluation dataset to measure any performance gap. Fine-tuning should only be considered for narrow, stable, high-volume tasks where evaluations confirm its necessity, after other methods have been exhausted.