AI Basics Machine Learning Large Language Models Intermediate

Fine-tuning

Pronunciation: /faɪn ˈtjuːnɪŋ/

The process of further training a pre-trained AI model on specialised data to improve performance on specific tasks.

Plain English Explanation

Fine-tuning takes an already-trained AI model and trains it further on a smaller, task-specific dataset. This adapts the model's general capabilities to perform better on particular domains, styles, or tasks — like teaching a general practitioner to specialise in cardiology.\n\nFine-tuning is distinct from prompt engineering (guiding the model at inference time) and from training a model from scratch.

Analogy

Fine-tuning is like hiring an experienced chef and teaching them your restaurant's specific recipes. They already know how to cook; you are refining their skills for your particular kitchen.

How is it used?

Organisations fine-tune models for brand-specific writing, domain-specific analysis (legal, medical), code generation in proprietary languages, and customer service with company-specific knowledge.

Real-world Example

A healthcare company fine-tunes an LLM on medical literature and clinical notes so it can assist doctors with terminology and context specific to their speciality.

Common Misconceptions

Fine-tuning does not give the model new factual knowledge in a reliable way — for that, RAG is often more appropriate. Fine-tuning changes behaviour and style more than facts.

History

Fine-tuning has been a core technique in machine learning for decades. With LLMs, parameter-efficient methods like LoRA (2021) made fine-tuning accessible without massive compute resources.

Related Terms

LLM Prompt