← Concept IndexDEFINITION WHY IT MATTERS COMMONLY CONFUSED WITH
In-context learning
Also called: learning from the prompt, few-shot at use time
A model's ability to 'learn' a task from instructions and examples placed in the prompt, adjusting its behaviour for that conversation without any change to its weights.
It's why giving examples in the prompt works so well and so immediately — and why the effect vanishes when the conversation ends. It's the cheap, reversible alternative to fine-tuning for shaping behaviour.
Fine-tuning or permanent learning. In-context learning lasts only for that context window; the model hasn't actually been changed.