---
title: "Discussion on \"Fine-Tuning vs. Prompting: When Do You Actually Need to Fine-Tune?\" | Hashnode"
description: "Discussion on \"Fine-Tuning vs. Prompting: When Do You Actually Need to Fine-Tune?\". Fine-Tuning vs Prompting: When You Actually Need It"
url: "https://hashnode.com/posts/fine-tuning-vs-prompting-when-do-you-actually-need-to-fine-tune/6ab251260f9381218203fbd0"
---

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# [Fine-Tuning vs. Prompting: When Do You Actually Need to Fine-Tune?](https://nagspidey.hashnode.dev/fine-tuning-vs-prompting-when-do-you-actually-need-to-fine-tune?utm_source=hashnode&utm_medium=feed)

Fine-Tuning vs Prompting: When You Actually Need It

Sep 22 · 8 min read · nagspidey.hashnode.dev  
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## Written by

[Nagappan S](https://hashnode.com/@imkarthik03)

@imkarthik03 · Chennai, India

  
[View profile](https://hashnode.com/@imkarthik03)

Building scalable products at the intersection of Full Stack Engineering & AI

I build scalable web apps, experiment with AI, and turn complex engineering problems into working products.

## Responses 2

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[Mihai\_LeanZero](https://hashnode.com/@mihaileanzero)

[Sep 22](https://hashnode.com/posts/fine-tuning-vs-prompting-when-do-you-actually-need-to-fine-tune/6ab251260f9381218203fbd0/comment/6ab29b1bbd3da03853076b40)

Good framing, especially the two failure-mode stories bookending it. One case worth adding to the list of what makes fine-tuning worth it: distillation for cost, not just consistency. Fine-tuning a much smaller, cheaper model on a larger model's outputs is one of the most common practical reasons teams reach for it, and it's a different economic argument than the prompt-got-too-long story here -- you're not shrinking latency or formatting errors on the same model, you're trading a one-time training cost for permanently cheaper inference on a task narrow enough for a small model to nail once it's been shaped to it. Worth its own line next to "narrow, high-volume, well-defined task," since the decision to drop down a model size class is a separate call from the decision to fine-tune at all.

[Nagappan S](https://hashnode.com/@imkarthik03)

[Sep 24](https://hashnode.com/posts/fine-tuning-vs-prompting-when-do-you-actually-need-to-fine-tune/6ab251260f9381218203fbd0/comment/6ab29b1bbd3da03853076b40#reply-6ab4c89d50e4c0b663bebaeb)

Thanks, Mihai. That's a useful addition, and you're right that the cost argument for distillation is a different case from the consistency stories I used. Deciding to drop down a model size class is a separate call from deciding to fine-tune at all.