applications

Fine-tuning vs RAG

A common architectural decision in AI application design. Fine-tuning modifies model weights for specialized behavior, while RAG retrieves external information at inference time. The choice depends on whether the need is for behavioral change (fine-tuning) or knowledge augmentation (RAG).

In practice

A company would use RAG for current product documentation but fine-tuning to change the model's communication style.