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PRANJUL RATHOUR
PRANJUL RATHOUR

Posted on Originally published at pranjulrathour.scult.in

Evaluating fine-tuning ROI: was the project actually worth the compute and time?

#finetuning #evaluation #roi

A fine-tuning project that ships doesn't automatically mean fine-tuning was the right call. It's worth honestly comparing the result against the simpler alternatives that were available from the start.

The comparison to actually run

  • Same eval set, three ways: the base model with a good prompt, the base model with few-shot examples, and the fine-tuned model.
  • Measure quality difference alongside cost difference — a marginal quality gain that cost forty GPU-hours may not have been worth it against a well-engineered prompt.
  • Factor in maintenance: a fine-tuned model needs retraining as requirements evolve; a prompt can be edited in seconds.

When the ROI question has an easy answer

If the task structurally can't be solved by prompting at all — a fixed classification head, a highly consistent generation format prompting can't reliably hit — the ROI question resolves itself. The interesting cases are the ones where prompting was actually close.

See fine-tuning vs a longer prompt: a real cost comparison.

About Pranjul Rathour

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Presenting to a room

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Requirements gathering, on stage

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Pranjul Rathour

Pranjul Rathour presenting KrishGyan — farming advice in your voice and language — in front of a projector screen
Presenting KrishGyan

Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-11.

Reach out if you want to talk GenAI, book a campus session, or invite him to judge:

Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
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