Training & Fine-tuning
Fine-tuning
Further training a pre-trained model on specific data to specialise it for a task.
Definition
Fine-tuning takes an existing pre-trained model and continues training it on a smaller, more focused dataset. This allows the model to develop expertise in a specific domain (legal documents, customer service for a particular industry, or a company's internal knowledge base) without losing its broad capabilities. Fine-tuning is significantly cheaper than training from scratch and is how most businesses customise AI for their context.
Related Terms
Pre-training
The initial, large-scale training phase where a model learns from massive datasets.
LoRA (Low-Rank Adaptation)
An efficient fine-tuning technique that modifies only a small part of a model's weights.
Supervised Learning
Training where the model learns from labelled examples, input paired with the correct answer.
Catastrophic Forgetting
When a model loses previously learned knowledge after being fine-tuned on new data.
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Disclaimer
This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.
Document reference: ISO_webpage_knowledge-base_glossary_v1
Last modified: 29 March 2026
Knowledge Base·Training & Fine-tuning·Fine-tuning