Fine-Tuning vs. RAG: Which Approach When?

Both approaches adapt an LLM to your needs — but in different ways.

RAG (Retrieval-Augmented Generation)

How: store documents externally, embed relevant chunks in the prompt on request.
Advantages: current, explainable (sources), no expensive training runs, immediately changeable.
Disadvantages: retrieval quality is crucial, more latency and tokens.

Fine-tuning

How: continue training model weights on your data (e.g. LoRA, QLoRA).
Advantages: better style/format, domain-specific behavior, fewer tokens per request.
Disadvantages: expensive, must be retrained when data changes, overfitting risk.

Decision

RAG when: knowledge changes, sources matter, no training budget — this is the case for most companies.

Fine-tuning when: format/style counts, the model does the same thing constantly (e.g. classification, code conversion), answers should be shorter.

Both: in practice often combined: fine-tuning for behavior + RAG for knowledge.