What is fine-tuning in LLMs?

Updated May 16, 2026

Short answer

Fine-tuning is training a pre-trained LLM on domain-specific data to improve performance on specialized tasks.

Deep explanation

Fine-tuning adjusts model weights using labeled or instruction data to adapt general-purpose LLMs for specific domains like legal, medical, or customer support. It can be full fine-tuning or parameter-efficient methods like LoRA.

Real-world example

A medical chatbot fine-tuned on clinical notes.

Common mistakes

Fine-tuning without sufficient high-quality data.

Follow-up questions

What is LoRA?
When should you avoid fine-tuning?

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