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 actually requires.

This whitepaper presents the decision framework Tenten AI has developed across 20+ enterprise engagements. The framework is not prescriptive: there are cases where fine-tuning is clearly correct, cases where RAG is clearly correct, and cases where both are needed. The goal is to give teams the vocabulary and criteria to make the decision deliberately rather than by default.

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.