Fine-tuning एक pre-trained model को एक छोटे, specialized dataset पर आगे train करके किसी specific task या domain के लिए अनुकूलित करता है। यह एक general model को specific needs के लिए customize करता है — हालाँकि कई मामलों में, prompting या RAG सरल और पर्याप्त होता है।
What fine-tuning is
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FINE-TUNING → take a PRE-TRAINED model and train it further on YOUR specific data:
→ starts from a general model (already knows a lot) → adapts it to a specific task/domain/style
→ uses a smaller, task-specific dataset (vs training from scratch)
→ produces a customized model specialized for your need
→ customize a general model for specific tasks/domains/styles
When to use fine-tuning (vs alternatives)
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FINE-TUNING → when you need:
→ a specific STYLE/format/behavior consistently; specialized task performance; a particular
tone/domain
→ and prompting alone isn't enough
ALTERNATIVES (often simpler/better):
→ PROMPTING → for many tasks, good prompts suffice (no training) — try this first
→ RAG → for adding KNOWLEDGE/data → retrieval, not fine-tuning (fine-tuning teaches
behavior/style, not great for injecting facts/knowledge)
→ fine-tuning for BEHAVIOR/style/task adaptation; RAG for KNOWLEDGE; prompting for most things