Training & Fine-tuning
Instruction Tuning
Fine-tuning a model specifically to follow instructions better.
Definition
A base language model trained to predict text isn't naturally good at following instructions — it's just good at continuing text. Instruction tuning is a fine-tuning step where the model is trained on examples of instructions paired with ideal responses. This is what transforms a raw language model into a practical assistant that actually does what you ask. Most commercially available AI assistants have undergone instruction tuning.
Related Terms
Fine-tuning
Further training a pre-trained model on specific data to specialise it for a task.
RLHF (Reinforcement Learning from Human Feedback)
Teaching an AI to improve its responses using human ratings to align it with human preferences.
Supervised Learning
Training where the model learns from labelled examples — input paired with the correct answer.
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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·Instruction Tuning