Fine-tuning vs RAG

Fine-tuning and RAG are the two main routes to company-specific AI, and they solve different problems. Fine-tuning trains the model further on your examples, changing its style, format and behaviour — teaching it how to respond. RAG retrieves your documents at question time, changing what the model knows in the moment — teaching it what to respond with.

Executives should care because choosing wrongly is expensive. Fine-tuning sounds like the premium option but does not reliably teach facts, requires curated training data, and ages as soon as your information changes. RAG is faster to deploy, always current with your documents, and provides citations — which is why the standard advice is: default to RAG, add fine-tuning only when tone, format or specialised behaviour genuinely demands it.

Concrete example: a firm wants an assistant that answers policy questions correctly — that is RAG. The same firm wants outbound letters to always follow its house style and structure — that is a fine-tuning (or careful prompting) job.

Rule of thumb: knowledge problems are retrieval problems; behaviour problems are training problems.