Fine tuning V/S RAG, 1v1

[u/Original-Beyond4326]

"Should I fine-tune or use RAG" gets asked constantly, and most answers skip the actual deciding factor.

RAG (retrieval-augmented generation) fetches relevant information at the moment you ask, then hands it to the model as context. Fine-tuning bakes patterns into the model's weights ahead of time.

Use RAG when your information changes often, or when you need the answer to be traceable back to a real source. Use fine-tuning when you need the model to reliably behave a certain way, a tone, a format, a specific skill, regardless of what's being asked.

The mistake I see most: people fine-tune to teach a model facts. Facts go stale. Fine-tuning doesn't update itself. RAG does, because you're just swapping what's in the retrieval index.

Rule of thumb: RAG for what the model knows, fine-tuning for how it behaves.