Expert knowledge for digital decisions

Finetuning or RAG – what is the right approach?

Short answer

For corporate knowledge almost always RAG: The model searches in your documents. Finetuning changes the model itself and is suitable for style and format, not for factual knowledge. New information requires with RAG a new document, with finetuning a new training run.

The Difference

RAG. Before answering, the documents are searched, the relevant sections are passed on. The model remains unchanged.

Finetuning. The model is further trained with examples and changes permanently.

Why RAG is better for knowledge

	RAG	Finetuning
New information	Add document	New training run
Source attribution	Possible	Not possible
Rights per document	Possible	Not possible
Remove incorrect information	Delete document	Laborious
Costs	Low	Much higher

Source attribution is the decisive point: A finetuned model cannot say where it got something from. For business information, this is an exclusion criterion.

When finetuning is useful

A specific tone or style that instructions cannot reliably produce
A fixed output format
A very specialized jargon
Cost reduction: A smaller, fine-tuned model can replace a larger one

The most common misconception

"We train the model with our manuals, then it knows our products." This does not work reliably. Finetuning shapes behavior, not factual knowledge – and what the model learns cannot be checked or corrected intentionally.

Recommendation

Start with RAG. Consider finetuning only when RAG works and a specific style or cost issue remains.

Key facts

For factual knowledge: RAG
For style and format: Finetuning
Key advantage of RAG: Source attribution possible

What we offer on this topic

We start with a clearly defined use case and integrate AI into your existing processes.

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Sources

All external claims are backed by traceable sources.

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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Lewis et al. / arXiv