AI Dictionary › Prompting
Many-shot prompting is a technique in which the prompt includes a large number of worked examples, from dozens to hundreds or even thousands, so the model learns the task pattern directly from the demonstrations. It extends the few-shot idea: where few-shot gives a handful of examples, many-shot gives as many as the context window allows.
The examples are placed in the prompt as input-output pairs before the real query. The model performs in-context learning, inferring the rule from the many demonstrations without any change to its weights. More examples generally help until the effect saturates or the context budget runs out.
Many-shot is useful for classification, strict formatting, matching a house style, and tasks where a few examples are not enough to pin down the desired behaviour. It can lift performance on tasks that few-shot handles poorly, at the cost of a longer and more expensive prompt.
Many-shot became practical only when context windows grew large enough to hold hundreds of examples. It was studied and named around 2024, notably in research on many-shot in-context learning, as long-context models made the approach feasible.
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