RAG or fine-tuning: which method suits which goal?
By Kevin Kröger, Geschäftsführer, Software und Plattformbetrieb
RAG is particularly suitable if answers are to be based on changeable or verifiable sources of knowledge. Fine-tuning is more suitable when behavior, format or a recurring task needs to be adjusted. Both procedures can be combined and do not replace evaluation.
When is RAG a more appropriate first step?
Retrieval-Augmented Generation searches for content that matches the query in an external knowledge base and provides it to the model as context. The basic RAG paper combines parametric model knowledge with a non-parametric memory. The approach is particularly interesting for business applications if sources need to be updateable, separable and the response should remain traceable. Retrieval quality, permissions and source coverage still need to be measured.
When does fine tuning help?
Fine-tuning changes model parameters using suitable training examples. It can adapt behavior, style, classification or a stable output format to a recurring task. LoRA shows a lower-parameter adaptation path in which base weights are frozen and additional trainable matrices are used. Current factual knowledge or access rights are not automatically and reliably resolved.
How do you make decisions without a technical reflex?
Start with test cases and measurable error types. If the model lacks current internal facts, first check retrieval and source quality. If it repeatedly fails to achieve stable behavior or format despite sufficient context, fine-tuning can be examined. Also compare prompting, tools, and deterministic logic. The smallest robust solution is often a combination rather than a single method.
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Frequently asked questions
- Can fine-tuning replace a knowledge base?
- Usually not useful for knowledge that changes frequently or requires citation. Changes are more difficult to update specifically and sources do not remain automatically traceable.
- Does RAG prevent wrong answers?
- No. Retrieval may return inappropriate, outdated, or incomplete content, and the model may use context incorrectly. Test cases, source display and quality limits remain necessary.