Can Fine-Tuning vs Prompting help in reducing "Hallucinations" in Data Science workflows?

All answers to this question.

This is a classic dilemma in Fine-Tuning vs Prompting. Hallucinations often happen because the model is trying to be "helpful" by filling in gaps. Prompting with "Chain-of-Thought" (CoT) is incredibly effective because it forces the model to show its work, which often catches its own errors. However, fine-tuning on a "reasoning" dataset—where the training data itself includes step-by-step logic—is even more powerful. If your data is very structured, fine-tuning the model to recognize those structures will drastically reduce the frequency of it making up fake data points or incorrect correlations.

   Answered 2025-06-12 by Heather Vargas

Does your dataset include a lot of numerical data, and if so, have you tried using tool-calling instead of just raw text generation?

   Answered 2025-06-13 by Scott Peterson

• We do have numbers, Scott. We've tried tool-calling for calculations, but the "interpretation" of why the numbers changed is where it fails. That is why we are looking at fine-tuning. We want the model to understand the context of our specific industry KPIs so it doesn't just describe a trend but explains it accurately based on our internal business logic.

     Commented 2025-06-14 by Thomas Gardner

For summaries, prompting with a very strict template usually works better than fine-tuning, which can sometimes make the model too rigid.

   Answered 2025-06-15 by Alice Montgomery

• That's a fair point, Alice. A strict template in a prompt is much easier to iterate on than a whole fine-tuned weights file.

     Commented 2025-06-16 by Christine Fowler