Teams building LLM-powered products almost always reach the same fork in the road: should we fine-tune a model on our data, or retrieve relevant context at query time?
Retrieval-augmented generation (RAG) excels when your underlying data changes frequently and you need traceability back to source documents. Fine-tuning shines when you need consistent tone, format, or domain-specific reasoning baked into the model itself.
In practice, most production systems we ship combine both: a fine-tuned model for voice and structure, layered with RAG for facts that change daily.
The decision ultimately comes down to three questions — how often does your data change, how important is source traceability, and what's your tolerance for retraining cost.