When should I choose Fine-tuning over RAG for domain accuracy?

All answers to this question.

You've hit on a key distinction. Fine-tuning is about behavior, while RAG is about knowledge. If your goal is to make the model speak like a doctor—using the right syntax, tone, and logical flow—fine-tuning is essential. However, if you want that "doctor" to know the specific patient history or the latest 2024 drug interactions, you need retrieval. In my medical tech role last year, we fine-tuned a base model on specialized medical journals to improve its "vocabulary" but still used a retrieval pipeline to fetch actual clinical guidelines. Don't view them as mutually exclusive; they solve different parts of the accuracy puzzle.

   Answered 2025-11-24 by Victoria Hayes

Victoria, for a startup on a budget, which one would you prioritize first to get a "minimum viable" level of accuracy?

   Answered 2025-11-26 by Samuel Porter

• Samuel, definitely start with RAG. It’s cheaper, faster to set up, and much easier to debug. You can see exactly which document caused a wrong answer, which is nearly impossible with a fine-tuned model.

     Commented 2025-11-27 by Douglas Grant

I think RAG is safer for compliance. You can control exactly what data the model sees, whereas fine-tuned weights are a "black box" that might leak sensitive info.

   Answered 2025-11-28 by Diane Murphy

• Great point, Diane. Data privacy is a huge reason why many enterprises are leaning toward retrieval-heavy architectures instead of retraining.

     Commented 2025-11-29 by Patrick Flynn