Stop Fine-Tuning for Everything: A Decision Tree for RAG vs Tuning vs Tools

Many teams default to fine-tuning their language models when facing problems, but this expensive approach often addresses the wrong issue. Teams should first identify whether their failures stem from missing knowledge (solved by RAG), lack of control over outputs (solved by tools and constraints), or actual task mismatches that require parameter changes. The most effective approach involves writing failing examples, labeling the failure types, and trying cheaper solutions first before considering fine-tuning as the final optimization step.