Can Hugging Face datasets provide better guidance than prompt engineering for fine-tuning?

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In specialized domains like medicine, prompt engineering will only get you so far because the model likely hasn't seen enough of your specific terminology during its base training. We found that using a high-quality, curated dataset from Hugging Face to perform Parameter-Efficient Fine-Tuning (PEFT) yielded significantly better results than even the most complex 10-shot prompts. By fine-tuning, you are essentially baking the domain knowledge into the model's weights rather than just trying to remind it of what it knows via a text window. It’s more work upfront, but the accuracy for diagnostic suggestions is night and day.

   Answered 2025-07-22 by Lisa Peterson

How are you handling the cleaning of those public datasets? I've noticed that some "expert" datasets on the Hub still have a lot of noise or formatting errors. Do you have a specific script or tool you recommend for auditing the data before you start the fine-tuning process?

   Answered 2025-08-10 by Mark Evans

• Mark, we actually use a "cleaner" LLM to scan the dataset first. We write a script that identifies outliers or inconsistent labels and flags them for human review. It’s a bit recursive using AI to fix the data for another AI, but it’s the only way to handle 100,000+ rows of medical notes without going crazy. This pre-processing is what actually makes the fine-tuning successful.

     Commented 2025-08-12 by Thomas Wood

Fine-tuning is definitely the way to go for medical apps. Prompts are too "volatile" and can change behavior with minor wording shifts that you can't afford in healthcare.

   Answered 2025-08-15 by Gary Wilson

• Exactly, Gary. The reliability you get from a fine-tuned model on a specific HF dataset is much higher than trying to "prompt-engineer" your way out of a hallucination.

     Commented 2025-08-18 by Lisa Peterson