Fine-Tuning vs RAG

The choice between fine-tuning and retrieval-augmented generation is the most frequently debated architectural decision in enterprise AI system design. It is also the most frequently made incorrectly; teams choose based on what is technically interesting rather than what the problem requires.

This whitepaper presents criteria for choosing fine-tuning, RAG, or both. The decision depends on the task, available training data, retrieval needs, and evaluation results.

The first and most important clarification: fine-tuning and RAG solve different problems. Fine-tuning changes what a model knows how to do. RAG changes what information is available during inference. Conflating these two problems is the source of most architectural mistakes in this space.