RAG vs. Fine-Tuning: What SMEs Really Need

The Myth of the Own Model

Many companies believe they have to 'fine-tune' a model so that it understands their business. This is often an expensive fallacy. Fine-tuning teaches the model new patterns, but no new factual knowledge. In addition, the knowledge becomes obsolete immediately after training.

RAG: The Model with the Open Book

Retrieval-Augmented Generation (RAG) is the pragmatic way. Instead of retraining the model, we give it access to your current documents. If you ask 'How is the turnover in Q3?', the AI looks into the database and answers. This is cheaper, faster, and more hallucination-free.

When Fine-Tuning Makes Sense

Fine-tuning is only worthwhile if you need a completely new language (e.g., a very specific dialect or code) or an extremely niche format. For everything else – knowledge management, support, analysis – RAG is the gold standard that we have perfected at Kivanto.ai.