1. If you have a large corpus of valuable data not available to the corporations, you can benefit from fine tuning using this data.
2. Otherwise just use RAG.
Fine-tuning makes sense when you need behavioral shifts (style, tone, bias) or are training on data unavailable at runtime.
RAG excels when you want factual augmentation without retraining the whole damn brain.
It's not either/or — it's about cost, latency, use case, and update cycles. But hey, binaries are easier to pitch on a slide.
1. If you have a large corpus of valuable data not available to the corporations, you can benefit from fine tuning using this data.
2. Otherwise just use RAG.