Two students both said 'I'm fine-tuning a model' — one meant a sentiment classifier, the other a support chatbot. Both are fine-tuning, but the data, model choice and evaluation are almost unrelated problems.
Classification
Needs a smaller model, a fixed label set, and a metric that's unambiguous: precision, recall, F1 per class. A confusion matrix tells you exactly which labels get confused with which — evaluation here is close to solved.
Open-ended generation
Has no fixed correct answer, so evaluation needs either human review, an LLM-as-judge rubric, or task-specific checks (does the reply contain a required disclaimer, is the format valid JSON). This is the harder, less settled half of the problem.
If your task is really classification, a smaller encoder-only model fine-tuned normally often beats fine-tuning a full generative LLM on cost and latency. See evaluating a fine-tuned model honestly.
About Pranjul Rathour

Talking through the products he has shipped
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 2026-09-07.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
- Email: [email protected]
- Invite / talk menu: https://pranjulrathour.scult.in/invite
- Portfolio & blog: https://pranjulrathour.scult.in
- LinkedIn: https://www.linkedin.com/in/pranjul-rathour/
- X: https://x.com/PranjulRathourx
- Instagram: https://www.instagram.com/pranjulrathour.in/
- Bluesky: https://bsky.app/profile/pranjulrathour.bsky.social
- GitHub: https://github.com/Pranjulrathour
Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: [email protected] · Invite me to your campus
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