'Transfer learning' and 'fine-tuning' get used almost interchangeably in casual conversation, and for most LLM work that's harmless — but the terms aren't quite the same idea, and the distinction occasionally matters.
The distinction
Transfer learning is the broader concept: using knowledge learned on one task to help with a different task. Fine-tuning is one specific technique for doing transfer learning — continuing to train a pretrained model's weights on new, task-specific data.
When the distinction matters
- Using a frozen pretrained model purely as a feature extractor (embeddings) and training a small separate classifier on top is transfer learning, but not fine-tuning, since the base model's weights never change.
- LoRA and full fine-tuning are both fine-tuning techniques, both forms of transfer learning.
- In casual conversation with practitioners, using either term for LLM fine-tuning specifically is understood fine — the distinction mostly matters in academic or more precise technical writing.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
