RAG vs fine-tuning: which one does your product actually need?
NEO Campus Editorial3 March 20266 min read
Retrieval-Augmented Generation and fine-tuning solve overlapping problems with very different trade-offs. Picking the wrong one wastes months.
Use RAG when knowledge changes
If the underlying information updates weekly or per-customer, RAG keeps answers fresh without retraining anything.
Use fine-tuning for behaviour
If you need a specific tone, format, or domain reasoning the base model lacks, fine-tuning is the right tool.
Combine them often
The strongest systems do both: fine-tune for style and structure, retrieve for facts.
