Prompting & Interaction

In-Context Learning

When a model adapts its behaviour based solely on examples provided in the current prompt.

Definition

In-context learning is the ability of a language model to perform new tasks based purely on examples given in the prompt — without any fine-tuning or weight updates. When you show a model a few examples of the task you want, it infers the pattern and applies it to new inputs within that session. This is the foundation of few-shot prompting and means that advanced models can be 'taught' new tasks in minutes through example selection alone.

Related Terms

Few-Shot Prompting

Giving an AI a small number of examples before asking it to perform a similar task.

Prompt Engineering

The practice of crafting inputs to get better outputs from AI models.

Fine-tuning

Further training a pre-trained model on specific data to specialise it for a task.

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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·Prompting & Interaction·In-Context Learning