RAG vs. Fine-tuning vs. Prompt engineering: 5 cues to choose the right approach for enterprise data
Originally published at https://svitla.com/blog/rag-vs-fine-tuning/?utm_source=adwords&utm_medium=ppc&utm_campaign=Search-Campaign_Brand_%D0%A1ompany&utm_term=svitla%20company Written by Patricio Gerpe - Senior Full Stack AI Engineer at Svitla Systems An internal assistant quotes a reimbursement policy that expired eighteen months ago. An employee acts on it. By the time the meeting happens, the…
RAG, fine-tuning, and prompt engineering are three techniques for handling enterprise data, each with distinct purposes and costs. Fine-tuning alters a model's default behavior, such as output format or domain vocabulary, but is slow to update and expensive. Prompt engineering, on the other hand, is a stateless interface layer that costs nothing to change but can only shape requests to within its limitations.
Retrieval Augmented Generation (RAG) is a dynamic memory system that injects current, accurate information at inference time.
When deciding which technique to use, consider how frequently the data changes and where the cheapest place to update it lies. If the data changes rapidly, like enterprise policy, prompt engineering is the cheapest solution. For factual recall, RAG is more effective than fine-tuning, which requires significant human effort and doesn't scale.
Fine-tuning can be useful for strict structural compliance or internal taxonomies, but distilled small models offer lower costs with similar performance. In short, understanding the nature of the data and its update frequency helps choose the most cost-effective solution.
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